Detection of electric discharges that precede fires in electrical wiring

ABSTRACT

Described herein are methods and systems for detecting electrical discharges that precede electrical fires in electrical wiring. One or more sensor devices coupled to a circuit detect one or more signal waveforms generated by electrical activity on the circuit. The sensor devices identify one or more transient signals within the one or more signal waveforms, and generate one or more transient characteristics based upon the identified transient signals. A server communicably coupled to the sensor devices receives the one or more transient characteristics. The server analyzes the one or more transient characteristics to identify one or more electrical discharge indications. The server generates one or more alert signals when one or more electrical discharge indications are identified.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/811,756, filed Mar. 6, 2020, which is a continuation of U.S. Pat. No.10,641,806, filed Mar. 20, 2019, which claims priority to U.S.Provisional Patent Application No. 62/645,743, filed on Mar. 20, 2018,the entirety of each of which is incorporated by reference herein.

TECHNICAL FIELD

The subject matter of the application relates generally to detection ofearly-stage electrical discharges, including arc faults, in electricalwiring.

BACKGROUND

Electricity travels over conductors (“wires”) on the grid, inside thehome (e.g., circuits, breaker panels, etc.), up to and inside connecteddevices and appliances. According to statistics released by the UnitedStates Fire Administration National Fire Data Center (available atwww.usfa.fema.gov/downloads/pdf/statistics/v19i8.pdf, see Table 7),electrical arcing in residential electrical wiring and outlets accountfor over 87% of electrical fires—which are one of the most dangerousthreats to life and property. Arc faults (also called arcs) arehigh-power, continuous electric discharges between two or moreconductors—typically occurring in residential buildings when theintegrity of an electrical wire or insulation is compromised (e.g.,through physical damage, water damage, corrosion, age, or looseconnections, among others). Events such as lightning strikes and powersurges can also initiate the breakdown of insulation and lead to acompromised wire. As a result of the compromised wire, small, sporadicelectrical discharges begin to occur and the insulating material thatsurrounds the wire is carbonized. As the electrical discharges continueover time, the insulation is increasingly eroded, and the electricaldischarges increase in intensity. Eventually, strong electricaldischarges become continuous arc faults that form in the wire—resultingin large flow of current and large releases of energy (withcorrespondingly high temperatures). Due to the proximity of the wire towood frame, insulation, and/or similar combustible materials, when thetemperatures produced by the arcs are high enough, they are likely toproduce fire. It would be a great advantage in preventing electricalfires if one could detect, and be warned about, the small electricaldischarges that may occur for days, weeks, or months before they becomelarge enough to create electrical arcing (as is described in Yereance,R. A., and Kerkhoff, T., Electrical Fire Analysis, 3rd ed., page 206,Charles C. Thomas, Springfield Ill. (2010)).

Electrical fires occur when a conductor fails to conduct, or aninsulator fails to insulate. Conductors fail to conduct because of jointfractures, the last strand of a cable breaks, an outlet, ‘push-in’ orother connection or junction on a conductor loosen, or in some other waythe surface area between the conductors becomes too small to serve as asafe and reliable connection. Consequently, the lack of a qualityconnection can result in a high resistance connection that produces heatand leads to physical and chemical processes that oxidize theconnection, further increasing resistance and heat. Electrical arcingoften occurs across these poor connections depending on many factors,including but not limited to physical changes in the connection(temperature), the energy load drawn across the connection, and thechanging quality (or lack of quality) of the connection.

Insulators fail where a flexible cord or rigid insulator fractures orpollutants or water infiltrates an intended insulation opening.Fractures may arise from defects in manufacturing, hammer blows duringconstruction, or repeated stress in use. Each time a small electricalfailure occurs across one of these fractures, a bit more of theinsulator is damaged, (usually) extinguishing the immediate failure but(usually) facilitating future failures. Most organic insulators “char,”slowly transforming them into more conductive simpler organic materialsthrough a process named “carbonization of insulation.” In other words,the job of the insulator is slowly compromised.

In turn, arcing along these carbonized paths produces “scintillations,”dim flashes of red, orange, yellow, or white light. Scintillations andthe fault currents associated with them are sporadic and highlyintermittent. As scintillations and fault currents evolve and becomemore active, they generate pulses with high-frequency content. Thesepulses propagate through the home's electrical network. This process,arc tracking, is a slow process that can take from a few weeks up tomany years. These electrical discharges may eventually become continuousarc faults, resulting in a large flow of current and large energyreleases (with correspondingly high temperatures) that can ignite afire.

Electrical arcing produces impulsive signals at ignition and extinctionmany times in each 60 Hz electrical cycle. Impulsive signals producebroadband electrical noise, including high-frequency energy thatpropagates freely through home wiring, which behaves as a communicationsnetwork at high frequencies.

The above-mentioned electrical discharges can occur in various ways,including: parallel, series, and line-to-ground discharges. A parallelelectrical discharge occurs when current/electrons flows from oneconductor to another through a gas or dielectric material because of alarge voltage difference between the conductors—typically throughdamaged insulation or the air. FIG. 1A is a diagram of a parallelelectrical discharge. The electrical circuit has two wires 102 a, 102 b,each of which is surrounded by an insulating material. If the insulationbetween the wires breaks down, then electrical discharges (such asdischarge 104) can occur between the wires. Examples of parallelelectrical discharges include carbonization (i.e., breakdown of theinsulating material) and wet tracking (i.e., moisture on the surface ofthe wire that enables currents to form).

A series electrical discharge occurs when a single conductor is damagedto an extent that resistance through the conductor is increased andcreates voltage differences high enough for discharges to occur withinthe conductor and into the surrounding insulation (or even to externalobjects, if the conductor is exposed). FIG. 1B is a diagram of a serieselectrical discharge. The electrical circuit includes a damaged wire 106that produces an electrical discharge 108. Examples of series electricaldischarges include ground pyrolysis (i.e., current flowing from theconductor to nearby wood), and last strand (i.e., breakage of the wireresulting in increase in heat and ignitable gases). A special type ofseries discharges occurs in a phenomenon known as a glowing connection(as shown in FIG. 1C). In such cases electrical conductors are touching,but not firmly connected together. An oxide layer forms at the boundaryof the interface which increases the resistance of the conductors at thejunction. If current is flowing through the interface, the temperaturecan rise to dangerous levels (e.g., the white area 110 shows a hightemperature at an electrical outlet) which can ignite nearby materialsand cause a destructive fire. By the time an electrical discharge hasprogressed to the point that it causes a fire, it is too late to takecorrective action and prevent loss. It is important to detect theoccurrence of such electrical discharges in electrical wiring as earlyas possible so that remedial measures can be put in place.

Technology such as arc-fault circuit interrupters (AFCI) currently existto detect early-stage electrical discharges, such as arc faults. Inelectrical outlets equipped with AFCI technology, an AFCI detects arcfaults in a circuit and breaks the circuit upon detection of such faultsto prevent an electrical fire from happening. However, AFCIs arerelatively expensive and must be installed on each circuit in a buildingto detect electrical discharges on the individual circuits.

Detecting electrical discharges on a circuit within an electricalinfrastructure is sufficient to finding and mitigating hazards whichcould become electrical fires, however a further improvement would be todevelop methods and technologies that allow customers and professionalsto more quickly identify when and where electrical discharges areoccurring.

SUMMARY

Therefore, what is needed is a method and system that detectsearly-stage electrical discharges before they are large enough to starta fire in electrical wiring systems, such as those found in homes andother buildings. The methods and systems described herein provide theadvantage of detecting early-stage electrical discharges in electricalwiring when they begin to form, which enables rapid notification of thepotential fire danger. In addition, the techniques described hereinenable the long-term automatic monitoring of electrical wiring systemsfor electrical discharges (e.g., weeks, months, years) to obtaindetailed information on activity and trends in a building's electricalwiring system—including the ability to distinguish between differentelectrical discharge severity levels, monitoring for events such assurges, sags and brownouts which can initiate carbonization ofinsulation and initiation of electrical discharges, correlate detectionof electrical discharges with the operation of appliances and devicesrunning in the home (for example, the arcing may occur within a devicesuch as an air conditioning unit; long-term monitoring provides theopportunity to correlate the discharges with the operation of the airconditioning unit, and the device can correlate electrical problems onthe electrical grid by aggregating information from monitoring acrossmultiple homes). Also, in some embodiments, the system leverages asingle monitoring device that plugs into an existing electricaloutlet—instead of complicated, expensive, or dangerous installation ofother devices or monitoring components.

The invention, in one aspect, features a system for detecting electricdischarges that precede electrical fires in electrical wiring. Thesystem comprises one or more sensor devices coupled to a circuit, eachsensor device is configured to detect one or more signal waveformsgenerated by electrical activity on the circuit. Each sensor device isconfigured to identify one or more transient signals within the one ormore signal waveforms, and each sensor device is configured to generateone or more transient characteristics based upon the one or moretransient signals. The system comprises a server computing devicecommunicably coupled to the one or more sensor devices. The servercomputing device is configured to receive the transient characteristicsfrom each of the sensor devices. The server computing device isconfigured to analyze the transient characteristics to identify one ormore electrical discharge indications. The server computing device isconfigured to generate one or more alert signals when one or moreelectrical discharge indications are identified.

The invention, in another aspect, features a computerized method ofdetecting electrical discharges that precede electrical fires inelectrical wiring. One or more sensor devices coupled to a circuitdetect one or more signal waveforms generated by electrical activity onthe circuit. Each sensor device identifies one or more transient signalswithin the one or more signal waveforms, and each sensor devicegenerates one or more transient characteristics based upon the one ormore transient signals. A server computing device communicably coupledto the one or more sensor devices receives the one or more transientcharacteristics from each of the sensor devices. The server computingdevice analyzes the transient characteristics to identify one or moreelectrical discharge indications. The server computing device generatesone or more alert signals when one or more electrical dischargeindications are identified.

The invention, in another aspect, features a sensor device for detectingelectrical discharges that precede electrical fires in electricalwiring, the sensor device coupled to a circuit. The sensor devicecomprises a module that senses electrical activity on the circuit anddetects one or more signal waveforms of the electrical activity. Thesensor device comprises a processor that identifies one or moretransient signals within the one or more signal waveforms, generates oneor more transient characteristics based upon the identified transientsignals, analyzes the transient characteristics to identify one or moreelectrical discharge indications, and generates one or more alertsignals when one or more electrical discharge indications areidentified.

The invention, in another aspect, features a system for detectingelectrical discharges that precede electrical fires in electricalwiring. The system comprises one or more sensor devices coupled to acircuit, each sensor device configured to detect one or more signalwaveforms generated by electrical activity on the circuit. The systemcomprises a server computing device communicably coupled to the one ormore sensor devices. The server computing device receives the one ormore signal waveforms from each sensor device. The server computingdevice analyzes the one or more signal waveforms to identify one or moreelectrical discharge indications. The server computing device generatesone or more alert signals when one or more electrical dischargeindications are identified.

The invention, in another aspect, features a computerized method ofdetecting electrical discharges that precede electrical fires inelectrical wiring. One or more sensor devices each coupled to a circuitdetect one or more signal waveforms generated by electrical activity onthe circuit. A server computing device communicably coupled to the oneor more sensor devices receives the one or more signal waveforms fromeach sensor device. The server computing device analyzes the one or moresignal waveforms to identify one or more electrical dischargeindications. The server computing device generates one or more alertsignals when one or more electrical discharge indications areidentified.

The invention, in another aspect, features a sensor device for detectingelectrical discharges that precede electrical fires in electricalwiring. The sensor device comprises a module that detects one or moresignal waveforms generated by electrical activity on the circuit. Thesensor device comprises a processor that receives the one or more signalwaveforms from each sensor device, analyzes the one or more signalwaveforms to identify one or more electrical discharge indications,generates one or more alert signals when one or more electricaldischarge indications are identified.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, the one or more signal waveforms comprisea full voltage cycle waveform. In some embodiments, the one or moresensor devices sample the full voltage cycle waveform at a frequency ina range between 10 MHz and 100 MHz.

In some embodiments, identifying one or more transient signals withinthe one or more signal waveforms comprises: a) dividing the samples ofthe full voltage cycle waveform into a plurality of bins; b) determininga mean value and a maximum value for each of the plurality of bins; c)determining a difference between the mean value and the maximum value;d) repeating steps a)-c) for each of a plurality of other samples of thefull voltage cycle waveform to determine an accumulated maximum valuefor each bin across all of the samples; and e) determining a derivativeof each accumulated maximum value across the plurality of bins. In someembodiments, generating one or more transient characteristics based uponthe identified transient signals comprises determining an averagetransient amplitude over a voltage cycle of the full voltage cyclewaveform, and determining an average transient amplitude for a pluralityof phase sections within the voltage cycle.

In some embodiments, analyzing the transient characteristics to identifyone or more electrical discharge indications comprises determining aratio of average peak transients in one or more of the phase sectionsnear a maximum voltage to average peak transients near a zero crossingof the voltage cycle, and identifying the average peak transients in oneor more of the phase sections near a maximum voltage as electricaldischarge indications, when the ratio is above a predeterminedthreshold. In some embodiments, generating one or more alert signalswhen one or more electrical discharge indications are identifiedcomprises determining a count of the identified electrical dischargeindications occurred within a predetermined amount of time, andgenerating one or more alert signals based upon the count of theidentified electrical discharge indications.

In some embodiments, identifying one or more transient signals withinthe one or more signal waveforms comprises a) determining a derivativeof the samples of the full voltage signal waveform across a full voltagecycle; b) dividing the samples of the full voltage cycle waveform into aplurality of bins; c) determining a maximum value for each of theplurality of bins; d) repeating steps a)-c) for each of a plurality ofother samples of the full voltage cycle waveform to determine anaccumulated maximum value for each bin across all of the samples; and e)determining a derivative of each accumulated maximum value across theplurality of bins. In some embodiments, generating one or more transientcharacteristics based upon the identified transient signals comprisesdetermining an average transient amplitude over a voltage cycle of thefull voltage cycle waveform, and determining an average transientamplitude for a plurality of phase sections within the voltage cycle.

In some embodiments, analyzing the transient characteristics to identifyone or more electrical discharge indications comprises determining aratio of average peak transients in one or more of the phase sectionsnear a maximum voltage to average peak transients near a zero crossingof the voltage cycle, and identifying the average peak transients in oneor more of the phase sections near a maximum voltage as electricaldischarge indications, when the ratio is above a predeterminedthreshold. In some embodiments, generating one or more alert signalswhen one or more electrical discharge indications are identifiedcomprises determining a count of the identified electrical dischargeindications occurred within a predetermined amount of time, andgenerating one or more alert signals based upon the count of theidentified electrical discharge indications.

In some embodiments, the one or more sensor devices sample the fullvoltage cycle waveform at 80 MHz upon detecting that the one or moresignal waveforms have reached a threshold value. In some embodiments,identifying one or more transient signals within the one or more signalwaveforms comprises identifying one or more samples of the full voltagecycle waveform that exceed a threshold value, and storing the identifiedone or more samples. In some embodiments, generating one or moretransient characteristics based upon the identified transient signalscomprises, for each identified sample: determining a count of peaks inthe identified sample; determining a rise time of the peaks in theidentified sample; determining a pulse width of the identified sample;and determining an integral of the identified sample. In someembodiments, analyzing the transient characteristics to identify one ormore electrical discharge indications comprises categorizing theidentified sample as an electrical discharge indication when the countof peaks in the identified sample is above a predetermined threshold andwhen the rise time of the peaks in the identified sample is above apredetermined threshold. In some embodiments, generating one or morealert signals when one or more electrical discharge indications areidentified comprises determining a count of the identified electricaldischarge indications occurred within a predetermined amount of time,and generating one or more alert signals based upon the count of theidentified electrical discharge indications.

In some embodiments, the one or more sensor devices identify one or moretransient signals within the one or more signal waveforms and generateone or more transient characteristics based upon the identifiedtransient signals using a transient detection profile stored in a memorymodule of the sensor device. In some embodiments, the server computingdevice generates an updated transient detection profile based upontransient characteristics received from one or more of the sensordevices and transmits the updated transient detection profile to each ofthe one or more sensor devices. In some embodiments, the one or moresensor devices apply the updated transient detection profile to identifysubsequent transient signals within the one or more signal waveforms andgenerate the one or more transient characteristics.

In some embodiments, the server computing device transmits the one ormore alert signals to a remote computing device. In some embodiments,the server computing device transmits the one or more alert signals toat least one of the one or more sensor devices. In some embodiments, theat least one of the one or more sensor devices activates a visualindicator upon receiving at least one of the one or more alert signals.In some embodiments, the visual indicator is a light emitting diode(LED) component of the at least one of the one or more sensor devices.In some embodiments, the server computing device uses one or moremachine learning algorithms to analyze the one or more signal waveformsto identify one or more electrical discharge indications.

The invention, in another aspect, features a computerized method ofdetecting hazardous electrical discharge activity patterns in electricalwiring. One or more sensor devices, coupled to a circuit, sense amultiple voltage cycle waveform generated by electrical activity on thecircuit. A computing device coupled to the sensor devices detects apattern of electrical discharge activity occurring in the multiplevoltage cycle waveform based upon transient characteristics of thewaveform in each of a plurality of cycles. The computing devicerecognizes the pattern of electrical discharge activity as a hazardevent. The computing device identifies one or more conditions related tothe pattern of electrical discharge activity. The computing devicegenerates one or more alert messages based upon the pattern ofelectrical discharge activity and the identified conditions.

The invention, in another aspect, features a system for detectinghazardous electrical discharge activity patterns in electrical wiring.The system includes one or more sensor devices coupled to a circuit, anda computing device coupled to the sensor devices. The sensor devicessense a multiple voltage cycle waveform generated by electrical activityon the circuit. The computing device detects a pattern of electricaldischarge activity occurring in the multiple voltage cycle waveformbased upon transient characteristics of the waveform in each of aplurality of cycles. The computing device recognizes the pattern ofelectrical discharge activity as a hazard event. The computing deviceidentifies one or more conditions related to the pattern of electricaldischarge activity. The computing device generates one or more alertmessages based upon the pattern of electrical discharge activity and theidentified conditions.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, detecting the pattern of electricaldischarge activity occurring in the multiple voltage cycle waveformcomprises detecting an instance of electrical discharge activity in eachof a plurality of cycles of the multiple voltage cycle waveform;comparing the detected electrical discharge activity instances usingcharacteristics of each electrical discharge activity instance todetermine a match; and grouping the matching electrical dischargeactivity instances into a patterns of electrical discharge activity. Insome embodiments, the transient characteristics comprise one or more of:a ratio of (i) average peak transients in one or more phase sections ofthe waveform cycle near a maximum voltage to (ii) average peaktransients near a zero crossing of the waveform cycle, and an average ofpeak transients in one or more of the phase sections near a maximumvoltage when the ratio is above a predetermined threshold. In someembodiments, the computing device determines a cause of the pattern ofelectrical discharge activity using the identified conditions.

In some embodiments, the conditions related to the pattern of electricaldischarge activity comprise one or more of: atmospheric conditions at alocation of the sensor devices, temporal conditions, electrical activityconditions associated with an electrical device coupled to the circuit,or power quality conditions associated with a power distribution systemto which the sensor devices are coupled. In some embodiments, theatmospheric conditions comprise one or more of precipitation, windactivity, solar activity, or outdoor temperature. In some embodiments,the temporal conditions comprise a discrete time of day or a continuousperiod of time. In some embodiments, the power quality conditions areone or more of: a power surge or a power sag. In some embodiments, thepower quality conditions are identified on a power distribution systeminside a building where the sensor devices and the circuit are located.In some embodiments, the power quality conditions are identified on apower distribution system that provides power to a building where thesensor devices and the circuit are located. In some embodiments, theelectrical activity conditions comprise one or more of: the electricaldevice turning on or the electrical device turning off.

In some embodiments, the computing device transmits the generated alertmessages to one or more remote computing devices. In some embodiments,one or more of the sensor devices receives at least one of the alertmessages and activates an alert indication device embedded in the sensordevice upon receiving the alert message. In some embodiments, the alertindication device comprises a light emitting diode (LED).

The invention, in another aspect, features a computerized method ofdetecting hazardous electrical discharge activity patterns in electricalwiring. One or more sensor devices, coupled to a circuit, sense amultiple voltage cycle waveform generated by electrical activity on thecircuit. A computing device, coupled to the sensor devices, detects apattern of electrical discharge activity occurring in the multiplevoltage cycle waveform based upon transient characteristics of thewaveform in each of a plurality of cycles. The computing deviceclassifies the detected pattern of electrical discharge activity as ahazard event based upon a similarity between the detected pattern ofelectrical discharge activity and one or more known patterns ofhazardous electrical discharge activity. The computing device generatesone or more alert messages based upon the classified pattern ofelectrical discharge activity.

The invention, in another aspect, features a system for detectinghazardous electrical discharge activity patterns in electrical wiring.The system includes one or more sensor devices coupled to a circuit anda computing device coupled to the sensor devices. The sensor devicessense a multiple voltage cycle waveform generated by electrical activityon the circuit. The computing device detects a pattern of electricaldischarge activity occurring in the multiple voltage cycle waveformbased upon transient characteristics of the waveform in each of aplurality of cycles. The computing device classifies the detectedpattern of electrical discharge activity as a hazard event based upon asimilarity of the detected pattern of electrical discharge activity toone or more known patterns of hazardous electrical discharge activity.The computing device generates one or more alert messages based upon theclassified pattern of electrical discharge activity.

Any of the above aspects can include one or more of the followingfeatures. In some embodiments, the one or more known patterns ofhazardous electrical discharge activity include a pattern of circuitbreaker arcing within an electric panel, a pattern of electric devicearcing, and a pattern of power supply arcing. In some embodiments, theone or more known patterns of hazardous electrical discharge activityare stored as two-dimensional images in a database communicativelycoupled to the computing device. In some embodiments, the computingdevice generates a two-dimensional image corresponding to the detectedpattern of electrical discharge activity occurring in the multiplevoltage cycle waveform. In some embodiments, classifying the detectedpattern of electrical discharge activity as a hazard event comprisescomparing one or more characteristics of the two-dimensional image ofthe detected pattern of electrical discharge activity to one or morecharacteristics of the two-dimensional images of the known patterns ofhazardous electrical discharge activity to determine the similarity. Insome embodiments, classifying the detected pattern of electricaldischarge activity as a hazard event comprises executing a machinelearning image classification model using the two-dimensional image ofthe detected pattern of electrical discharge activity as input togenerate the similarity to one or more of the two-dimensional images ofthe known patterns of hazardous electrical discharge activity. In someembodiments, the machine learning image classification model is basedupon a convolutional neural network.

In some embodiments, the computing device transmits the generated alertmessages to one or more remote computing devices. In some embodiments,one or more of the sensor devices receives at least one of the alertmessages and activates an alert indication device embedded in the sensordevice upon receiving the alert message. In some embodiments, the alertindication device comprises a light emitting diode (LED). In someembodiments, the computing device identifies one or more conditionsrelated to the detected pattern of electrical discharge activity. Insome embodiments, the computing device generates the one or more alertmessages based upon the classified pattern of electrical dischargeactivity and the identified conditions.

Other aspects and advantages of the invention will become apparent fromthe following detailed description, taken in conjunction with theaccompanying drawings, illustrating the principles of the invention byway of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the technology described above, together with furtheradvantages, may be better understood by referring to the followingdescription taken in conjunction with the accompanying drawings. Thedrawings are not necessarily to scale, emphasis instead generally beingplaced upon illustrating the principles of the technology.

FIG. 1A is a diagram of a parallel arc fault.

FIG. 1B is a diagram of a series arc fault.

FIG. 1C is a diagram of a glowing connection.

FIG. 2 is a block diagram of a system for detecting electricaldischarges that precede electrical fires in electrical wiring.

FIG. 3 is a diagram of an exemplary electrical transmission network.

FIG. 4A is a diagram of field-collected measurements of an exemplarycommunication channel for indoor, residential power-line networks.

FIG. 4B is a diagram of transfer characteristics for three differentcircuits and a comparison 20 m long cable measured in a lab setting.

FIG. 5 is a flow diagram of a computerized method of detectingelectrical discharges that precede electrical fires in electricalwiring.

FIG. 6A is a detailed flow diagram of a computerized method of detectingelectrical discharges that precede electrical fires in electricalwiring.

FIG. 6B is a detailed flow diagram of a computerized method of detectingelectrical discharges that precede electrical fires in electricalwiring.

FIG. 6C is a detailed flow diagram of a computerized method of detectingelectrical discharges that precede electrical fires in electricalwiring.

FIGS. 7A-7C are diagrams of exemplary waveform data generated by thesensor device that depict transients indicative of electrical dischargeactivity.

FIGS. 8A-8C are diagrams of exemplary waveform data generated by thesensor device for identifying transient characteristics.

FIG. 9 is a diagram of a transient amplitude ratio over time.

FIG. 10 is a diagram of a likelihood of electrical discharge activityoccurring over time.

FIG. 11 is a diagram of an exemplary user interface of a remotecomputing device for displaying an alert signal notification.

FIG. 12 is a diagram of exemplary waveform data for safety signalsgenerated by a testing device.

FIG. 13 is a block diagram of a proxy device that can be used inconjunction with the system of FIG. 2.

FIGS. 14A-14E are diagrams showing fire precursor arcing signalscollected by a sensor device installed in a test home.

FIG. 15 is a diagram showing more detail of the arcing signals of FIG.14C.

FIG. 16 is a diagram showing more detail of an individual pulse from anelectrical discharge.

FIG. 17 is a diagram with a number of graphs showing system testresults.

FIG. 18 is a histogram showing amplitude of pulses from laboratory datacompared with amplitude of pulses re-created by a proxy device.

FIG. 19 is a plot showing how fire precursor signals appear over time.

FIG. 20 is a plot for a device running on the electrical network that isgenerating HF electrical activity.

FIG. 21 is a diagram of graphs of root mean square (RMS) Voltage changesover time associated with an appliance turning on and off.

FIG. 22 is an exemplary visualization for an electrical dischargeactivity pattern, where the pattern is generated from the voltagechanges of FIG. 21 as detected by system 200.

FIG. 23 is another exemplary visualization for an electrical dischargeactivity pattern generated by system, including an unfiltered view and afiltered view.

FIG. 24A is another exemplary visualization for an electrical dischargeactivity pattern, where the pattern is generated due to arcing at anelectrical panel bus bar in a particular home and the pattern occurs attwo different time periods.

FIG. 24B is another exemplary visualization for an electrical dischargeactivity pattern, where the pattern is generated due to arcing at anelectrical panel bus bar in a different home than in FIG. 24A.

FIG. 25 is an exemplary heat map of electrical discharge activitypatterns occurring over time, using the electrical discharge activitypattern of FIG. 22.

FIG. 26 is another exemplary heat map of electrical discharge activitypatterns occurring over time, using the electrical discharge activitypattern of FIG. 24A.

FIG. 27 is a flow diagram of a computerized method of detectingelectrical discharges that occur in electrical wiring across multiplecycles of a voltage waveform.

FIG. 28 is a diagram of an exemplary use case timeline for detectingelectrical discharge patterns that occur in electrical wiring acrossmultiple cycles of a voltage waveform and identifying relatedconditions.

FIG. 29 is a flow diagram of a computerized method of detectingelectrical discharges that occur in electrical wiring across multiplecycles of a voltage waveform based upon known hazardous electricaldischarges.

DETAILED DESCRIPTION

FIG. 2 is a block diagram of a system 200 for detecting electricaldischarges that precede electrical fires in electrical wiring. Thesystem 200 includes electrical wires 202 that comprise a circuit in apower distribution system 220 (e.g., an electrical system in aresidential or commercial building) that transfers power received from aservice entrance cable 222 to branch circuits (such as the wires 202)which feed the power to appliances and outlets (e.g., outlet 208) in thebuilding. Electrical discharges create a very fast impulse of currentthat travel throughout the electrical power distribution system viatransmission line effects. The high frequency signal created by theelectrical discharge travels through the home much like the highfrequency signals use in power line communications.

Generally, the wires that provide power to circuits, outlets, devices,and appliances in buildings are the medium over which fire precursorsignals travel from origin to sensor. Precursors are impulsivedischarges and result in electromagnetic signals that travel along thepower distribution wires in a building. Every location in the wiring(both fixed house wiring and mobile cords to devices and appliances) hasa transfer function to every outlet in the house, and the precursorsignals arrive at the sensor modified by this transfer function. Themethods and systems described herein leverage methods similar to thosedeveloped in power line carrier communication techniques to measure andidentify very broad frequency content which travels through a home'selectrical network.

FIG. 3 is a diagram of an exemplary electrical network servicing one ormore residences. As shown in FIG. 3, electricity from a high voltagepower line 300 passes through an electrical transformer 302 and isdelivered to one or more residences (e.g., Residence A 304, Residence B306) through a +120V AC line (L1) 308, a neutral line 310, and a −120VAC line (L2) 312. The lines L1 and L2 are coupled to a plurality ofbranch circuits 314 via circuit breakers 316 in a service panel 318inside the residence. It should be appreciated that communication ispossible from any circuit on L1 to any circuit on L2.

Communicating using power lines has been considered and used since theturn of the 20th century. In the last twenty years, various companieshave released products that provide 100 Mbps to 1 Gbps local areanetwork communication over in-building electrical distribution systems.This work resulted in the standardization of power line communication inIEEE 1901 and the widespread availability of “ethernet over powerline”adapters for consumer use, and broadband communication over in-buildingpower lines is a mature technology.

There are two major issues associated with communicating over powerlines (i.e., the channel): the transmission and attenuationcharacteristics of a transmitter and receiver on different circuits ofthe building and the channel noise environment. The powerline channel ischallenging because there are many discontinuities due to branches(e.g., each outlet on a circuit) as well as loads with frequency andtime dependent impedances (e.g., due to different device power draw,construction, and on vs. off characteristics). The devices that areresponsible for the time variance are also responsible for both narrowand broad band noise injection due to switching power supplies and othernon-linear equipment elements. Exemplary techniques relating tocurrently available powerline communication is described in C. Cano, A.Pittolo, D. Malone, L. Lampe, A. M. Tonello, A. G. Dabak, “State of theart in power line communications: From the applications to the medium,”IEEE J. Sel. Areas Commun., vol. 34, pp. 1935-1952, July 2016 (availableat https://arxiv.org/pdf/1602.09019.pdf), which is incorporated hereinby reference.

Power line channel transmission characteristics have been the subject ofsignificant field measurement and modeling activities that culminated inthe IEEE 1901 standard ratification in 2010 (as described in “IEEEStandard for Broadband Over Power Line Networks: Medium Access Controland Physical Layer Specifications, IEEE Standard 1901-2010,” September2010. FIG. 4A shows field-collected measurements of an exemplarycommunication channel for indoor, residential power-line networks. Thetop chart in FIG. 4A represents multiple pairs of transmitters andreceivers on the same circuit, and the bottom chart in FIG. 4A showsmultiple transmitter and receiver pairs where the units are on differentcircuits (described in M. Tlich, A. Zeddam, F. Moulin, F. Gauthier,“Indoor power-line communications channel characterization up to 100MHz—Part I: One-parameter deterministic model,” IEEE Trans. Power Del.,vol. 23, no. 3, pp. 1392-1401, July 2008). Measurement results taken inSpain show similar characteristics confirming that changes in mainsconfiguration and wiring style result in similar overall characteristics(see C. Cano, et al., supra). The more interesting and difficult case isthe chart in FIG. 4B which shows the transfer characteristics for threedifferent circuits and a comparison 20 m long cable measured in a labsetting (described in E. Liu, Y. Gao, O. Bilal, and T. Korhonen,“Broadband characterization of indoor powerline channel,” Proc. Int.Symp. Power Line Commun., Zaragoza, Spain, 31 March-2 Apr. 2004).

The charts of FIGS. 4A and 4B show variable frequency dependence for aparticular channel but relatively flat attenuation on average. The sitesused for gathering these data were varied in size from apartments tolarge homes and in age of construction. The channel characteristics aresimilar in many ways to stationary wireless communication channels.Wireless channels experience fading (i.e., frequency, position, and timedependent attenuation) due reflections of signals off objects in theenvironment (i.e., multipath induced fading). The same effect is at playhere because discontinuities, mismatched impedances, and branches in thewiring result in signal reflections. In the time domain, thesereflections manifest themselves as amplitude and time shifted copies ofthe original signal arriving at the receiver. In the frequency domain,the frequency components of these copies either constructively ordestructively interfere causing peaks and notches in the transfercharacteristic as observed in the charts.

Power line communication systems use the same techniques for signalencoding and noise resilience that are employed in broadband, wirelesslocal area networks (e.g., IEEE 802.11n), chiefly orthogonal frequencydivision multiplexing (OFDM). OFDM is a good solution because it is atechnique for spreading information across a wide band of the spectrumwhile also reducing the impact of signal dispersion and fading.

The power line environment is challenging for communication, but marketsuccess of Ethernet over power line products demonstrate that it ispossible for broadband signals to traverse the complicated electricalnetwork. Narrow band communication is very challenging due to extremefrequency selectivity that is dependent on individual, time varyingchannel conditions. Circuit to circuit signal attenuations aresignificant but are typically less than 50 dB making it possible forsignals sourced almost anywhere in the electrical wiring to be detectedat a receiver elsewhere in the house wiring (i.e., at an electricalsocket).

Turning back to FIG. 2, the system 200 further includes a sensor device204 coupled via a 120 VAC plug 206 to an electrical outlet 208 of thebranch circuit wires 202. The sensor device comprises electroniccomponents (e.g., processing module 204 a, ADC 204 b, CPU 204 c) whichcouple the sensor device to the electrical infrastructure in a way thatallows the electrical discharge signals to be amplified while in someembodiments, also filtering out unwanted 60 Hz signal and electricalnoise generated by appliances running on the electrical system. Theprocessing module 204 a includes components such as capacitors,resistors, and amplifiers that sense electrical activity occurring onthe electrical wires 202 of the branch circuit and capture the sensedelectricity as waveform data. In some embodiments, the processing module204 a includes a filter that can limit a frequency response of thesensor device to a range in which electrical discharges have a highsignal-to-noise ratio. The filtering can be implemented using hardwarecomponents or in firmware installed on the processing module 204 a. Itshould be noted that the electrical activity occurring on electricalwires 202 of the branch circuit includes signals transmitted to and fromthe power distribution system 220. In this way, a single sensor iscapable of seeing electrical discharge signals throughout the fullelectrical distribution system 220, including the other branch circuits.Although FIG. 2 depicts a single sensor device 204, it should beappreciated that the system 200 can comprise two or more sensor devicespositioned to sense electrical activity in a power distribution system.Multiple sensors sending data to a server computing device can provideincreased sensitivity and work together to provide information on thelocation of where electrical discharges are occurring.

In some embodiments, location can be determined using time-of-arrivaltechniques, where the time difference of arrival of a transient signalto each sensor device provides a relative distance to the location ofthe electrical discharges on the wire. Generally, the sensor device 204is configured to see the shape of pulses. The pulse shapes the sensordevice sees are the source pulse shape plus reflections of that shapefrom every branch, termination, or impedance change in the electricalsystem wiring. The time between reflections is the product ofdifferences in distance divided by group velocity. The magnitude andpolarity of reflections is determined by the complex impedance of theload or junction. The timing of various reflections tell the location ofthe source in the circuit; the shape changes of those reflections tellabout the impedance of the loads and junctions that produced thereflections. For example, if two sensor devices are installed on thesame circuit at different locations, time differences of arrival at thetwo devices can determine locations in the circuit—even for pulses whosereflections cannot be seen above the noise.

In one embodiment, the sensor device 204 can capture up to 27 millionwaveform samples per second and allocate the samples into 512 bins. Thesensor device 204 determines a maximum amplitude in each of the bins andgenerates waveform data using the binned values. The sensor device 204also performs functions to obtain derivative values of the waveformsignals, in order to reduce the bandwidth and processing requirements ofthe sensor hardware. For example, the sensor device 204 can discardadjacent waveform samples to get the first derivative, and then repeatthe process to get the second derivative. In one embodiment, the sensordevice 204 adds several adjacent samples and subtracts several adjacentsamples to obtain a wavelet that is then analyzed againstpreviously-captured wavelets to determine whether any changes in thesignal have occurred.

The sensor device 204 is communicably coupled to a server computingdevice 214 via a communication network 212. In one embodiment, thesensor device 204 is equipped with communication components (e.g.,antenna, network interface circuitry) that enable the sensor device 204to communicate with the server computing device 214 via a wirelessconnection.

The communication network 212 enables the other components of the system200 to communicate with each other in order to perform the process ofdetecting electrical discharges in electrical wiring as describedherein. The network 212 may be a local network, such as WiFi or a LAN,or a wide area network, such as the Internet and/or a cellular network.In some embodiments, the network 212 is comprised of several discretenetworks and/or sub-networks (e.g., cellular to Internet) that enablethe components of the system 200 to communicate with each other.

The server computing device 214 is a combination of hardware, includingone or more special-purpose processors and one or more physical memorymodules, and specialized software modules—such as transient analysismodule 214 a—that are executed by a processor of the server computingdevice 214, to receive data from other components of the system 200,transmit data to other components of the system 200, and performfunctions for detecting electrical discharges in electrical wiring asdescribed herein. In some embodiments, the module 214 a is a specializedset of computer software instructions programmed onto a dedicatedprocessor in the server computing device 214 and can includespecifically-designated memory locations and/or registers for executingthe specialized computer software instructions. Further explanation ofthe specific processing performed by the module 214 a will be providedbelow. It should be appreciated that, in some embodiments, the sensordevice 204 can be configured to operate as a standalone device, in thatthe processing described herein with respect to the server computingdevice 106 can be performed by the sensor device 204 (i.e., a processorand memory can be embedded in the sensor device that conducts the datacollection, analysis, and alerting processes described herein).

The database 216 comprises transient and/or persistent memory for datastorage that is used in conjunction with the process of detectingelectrical discharges in electrical wiring as described herein.Generally, the database 216 is configured to receive, generate, andstore specific segments of data for use by the server computing device214. In some embodiments, all or a portion of the database 216 can beintegrated within the server computing device 214, or be located on aseparate computing device or devices. For example, the database 216 cancomprise a database such as MySQL™ available from Oracle Corp. ofRedwood City, Calif. In another example, the database 216 can comprise acloud-based storage medium, such as Amazon Web Services (AWS)™, thatuses DynamoDB™. In other embodiments, the database can be located inmemory, e.g., on the server computing device 214 and/or sensor module204.

FIG. 5 is a flow diagram of a computerized method 500 of detectingearly-stage arc faults in electrical wiring, using the system 200 ofFIG. 2. One or more sensor devices (e.g., sensor device 204) coupled toa branch circuit (e.g., Branch 1 circuit comprising wires 202) detect(502) one or more signal waveforms generated by electrical activity onthe branch circuit. For example, because an electrical discharge is animpulse change in current, the signal generated by the electricaldischarge transmits down the electrical wires 202 reflecting around anyjunction. These signals have a delay in returning, which is typicallybased upon the length of the wire. The signals also exhibit a phaseshift, which enables the system 200 to identify transient signals in thewaveform which represent activity that relates to an electricaldischarge (even a very small electrical discharge) on the electricalwires 202 as described herein.

The sensor device 204 identifies (504) one or more transient signalswithin the one or more signal waveforms that are detected. The sensordevice 204 generates (506) transient characteristics based upon theidentified transient signals and transmits the transient characteristicsto server computing device 214. The transient analysis module 214 areceives (508) the sets of transient characteristics from each sensordevice 204, and analyzes (510) the transient characteristics to identifyone or more electrical discharge indications. The alert generationmodule 214 b generates (512) one or more alert signals when one or moreelectrical discharge indications are identified.

It should be appreciated that, in some embodiments, the sensor device204 can detect the one or more signal waveforms (step 502) and transmitthe one or more signal waveforms to the transient analysis module 214 afor further processing, including identifying transient signals (step504), generating transient characteristics (step 506), analyzing thetransient characteristics (step 510), and generating alert signals (step512). It should further be appreciated that, in some embodiments, thesensor device 204 can perform all of the steps of FIG. 5 internally. Insome embodiments, the sensor device 204 compresses the one or moresignal waveforms prior to transmitting the signal waveforms to thetransient analysis module 214 a—in order to conserve network bandwidth,improve processing capacity and performance, and the like.

In some embodiments, the alert generation module 214 b transmits the oneor more alert signals to a remote computing device (e.g., a mobilephone, tablet, smart watch, and the like). The remote computing devicecan, e.g., display a message or indicator (such as a warning icon) on ascreen associated with the remote computing device based upon receipt ofthe one or more alert signals. For example, the alert signal cancomprise a packet-based message including a corpus of text thatindicates a dangerous condition or hazard as detected by the system 200.In some embodiments, the alert generation module 214 b transmits the oneor more alert signals to one or more of the sensor devices 204. Thesensor devices 204 can activate one or more components (e.g., embeddedcomponents in the sensor device) upon receipt of the one or more alertsignals. For example, upon receiving an alert signal from the alertgeneration module 214 b, the sensor device(s) 204 can activate an LEDelement that lights up and/or flashes on the exterior of the sensordevice 204 to indicate that a dangerous condition or hazard has beendetected by the system 200. Additional detail about the steps of FIG. 5is provided below with respect to FIGS. 6A to 6C.

FIGS. 6A to 6C comprise a detailed flow diagram of computerized methodsof detecting electrical discharges that precede electrical fires inelectrical wiring, using the system 200 of FIG. 2 and according to theframework described above with respect to FIG. 5. FIGS. 6A to 6C includethree different methods that may be used by the system 200 to detectelectrical discharges—Method A (FIG. 6A), Method B (FIG. 6B), and MethodC (FIG. 6C). It should be appreciated that these methods are exemplary,and other methods may be contemplated for use with the system describedherein. Also, it should be appreciated that the Methods A, B, and C maybe used independently or in conjunction with each other. In oneembodiment, the sensor device 204 may include multiple logical and/orphysical processors (e.g., CPU 204 c) that each processes the waveformdata according to one of the Methods A, B, or C.

For example, in Methods A and B, the high-speed analog-to-digitalconverter (ADC) 204 b of sensor device 204 detects waveforms (502) bycapturing a full voltage cycle waveform ( 1/60^(th) of a second),sampling at 27 MHz—while in Method C, the ADC 204 b captures a fullvoltage cycle waveform ( 1/60^(th) of a second), sampling at 80 MHz upondetecting that the waveform has reached or exceeded a threshold (so asto conserve bandwidth and memory resources).

The CPU 204 c of sensor device 204 can identify transients (504) in thewaveform data in several different ways. For example, in Method A, theCPU 204 c performs a binning process on the sampled waveform data bydividing the 27 million samples into 512 bins. The CPU 204 c calculatesthe difference between the maximum and the minimum of each bin andcalculates the difference between the maximum and the mean of each bin.Next, the CPU 204 c accumulates 15 cycles of binned maximum-mean dataand determines the maximum of the 15 cycles. Then, the CPU 204 ccalculates the derivative across the bins of each accumulated maximum.

In Method B, the CPU 204 c calculates the derivative of the waveformacross the full voltage cycle. The CPU 204 c divides the resulting 27million samples into 512 bins and calculates the maximum of each bin.Next, the CPU 204 c accumulates 15 cycles of binned maximum data anddetermines the maximum of the 15 cycles. Then, the CPU 204 c calculatesthe derivative across the bins of each accumulated maximum.

In Method C, the CPU 204 c triggers storage of waveform samples thatexceed a floating threshold including samples prior to the trigger—theseare single transients within the cycle.

Continuing with FIGS. 6A to 6C, the CPU 204 c of sensor device 204 cangenerate transient characteristics (506) in different ways. For example,in Methods A and B, the CPU 204 c calculates the average transientamplitude over the full voltage cycle, and calculates the averagetransient amplitude for 16 phase sections within the voltage cycle. InMethod C, the CPU 204 c counts the number of peaks in the transient,calculates the rise time of the peaks, calculates the maximum amplitudeof the transient, calculates the pulse width of the transient, andcalculates the integral of the transient.

When the sensor device 204 determines that a change has occurred in thevoltage waveform data based upon the binning process described above,the sensor device 204 transmits the transient characteristic data to theserver computing device 214 via network 212. The transient analysismodule 214 a receives (504) the transient characteristic data from thesensor device 204 and analyzes (506) the waveform data to identify oneor more electrical discharge indications in the signal waveform. In oneexample, the signal analysis module 214 a reconstructs the signalwaveform using the received data and compares the distribution oftransients (including where the transients occur in phase) to anexpected distribution of transients. For example, the signal analysismodule 214 a can determine where the transients occur in phase as wellas how repeatable the transients are—e.g., do the transients exhibit aregular pattern in the waveform (which may indicate operation of adevice or appliance on the circuit) or do the transients exhibit anirregular pattern (which may indicate electrical discharge and/or arcfault activity).

In some embodiments, the CPU 204 c of sensor device 204 transmits thedetermined transient characteristics to the transient analysis module214 a of server computing device 214, and the module 214 a receives thetransient characteristics. It should be appreciated that, in someembodiments, the sensor device 204 can be configured as a standalonemodule that does not require a connection to a server computing device214—in these embodiments, the sensor device 204 can also perform thesteps of analyzing the transient characteristics (510) and generatingalert signals (512), and further in some embodiments, the step oftransmitting alert signals to a remote computing device (as describedpreviously). In some embodiments, the alert signals can be generatedlocally within the sensor device 204 and used to trigger othercomponents of the sensor device 204—such as a speaker or other audiocomponent embedded in the sensor device 204, or a light or other visualcomponent (e.g., LED) embedded in the sensor device 204—to alert nearbypersons that potentially dangerous electrical activity is occurring.

The transient analysis module 214 a can analyze the transientcharacteristics (510) received from the sensor device 204 in differentways. For example, in Methods A and B, the module 214 a determines theratio of average peak transients in phase sections near maximum voltageto the average peak transients near voltage zero crossings. If the ratiois greater than a threshold value, the module 214 a identifies thesetransients as potential electrical discharges. In Method C, the module214 a evaluates whether the number of peaks in the transient exceeds apredefined threshold and whether the rise time is greater than apredefined threshold. If so, the module 214 a identifies the transientas a potential electrical discharge.

FIGS. 7A-7C and 8A-8C are diagrams of waveform data captured by CPU 204c of the sensor device 204 that depict transient signals. FIG. 7A is anexemplary diagram of a waveform for two milliseconds of sampled voltagedata, captured at 16 million samples per second. As shown in FIG. 7A,most of the waveform data is general electrical background noisegenerated by items such as appliances and electromagnetic signals in theair. However, the waveform data also exhibits several larger spikes(e.g., 702, 704)—which are transients that may indicate electricaldischarge activity in the electrical circuit.

FIG. 7B is an exemplary diagram that shows a portion of the waveformfrom FIG. 7A, enlarged to show additional detail. As seen in FIG. 7B,the shape of the individual transients (e.g., 712, 714) appears. Takingan even closer look, FIG. 7C is an exemplary diagram that shows aportion of the waveform (i.e., two μs) from FIG. 7B, enlarged to showadditional detail. As shown in FIG. 7C, the transient 514 exhibits aring-down structure, which indicates the amount of time it takes forattenuation of the signal. In addition, the transient 714 has very fastchanges in voltage and a fast rise time. As explained above for MethodC, the CPU 204 c of sensor device 204 can use this type of sampledwaveform data to generate transient characteristics (506) fortransmission to the transient analysis module 214 a.

FIGS. 8A-8C are exemplary diagrams that show how the CPU 204 c of sensordevice 204 can divide the sampled waveform signal into 512 bins (i.e.,as described above with respect to Methods A and B). An exemplary fullvoltage cycle waveform is shown in FIG. 8A, with the bin number assignedacross the x-axis and the amplitude represented on the y-axis. FIG. 8Bis another exemplary voltage cycle waveform, with annotations showinghow the CPU 204 c of sensor device can identify transients as describedabove with respect to Methods A and B. As shown in FIG. 8B, the CPU 204c can determine the current max, the current mean, and the max over 15cycles—as well as the max derivative over 15 cycles, the current maxderivative, and the derivative over the full voltage cycle. FIG. 8C isan exemplary voltage cycle waveform, with an annotation depicting apotential electrical discharge identified by the transient analysismodule 214 a. As described above with respect to Methods A and B, thetransient analysis module 214 a can determine the ratio of average peaktransients in phase sections near maximum voltage (e.g., section 802) tothe average peak transients near voltage zero crossings (e.g., section804). The transient analysis module 214 a can generate a representationof the ratio (see FIG. 9). As shown in FIG. 9, the time period from15:17:40 to 15:18:00 (indicated by 902) is when electrical dischargeswere being created on the electrical wiring.

In some embodiments, the transient analysis module 214 a can convert theratio data from FIG. 9 into a metric that indicates the likelihood ofelectrical discharges occurring based upon the ratio. FIG. 10 is anexemplary diagram showing the likelihood of electrical dischargesoccurring during a specified time period, based upon the ratio data fromFIG. 9. As shown in FIG. 10, the likelihood of electrical dischargesmoves from 0 to 2 at 15:17:40 and fluctuates between 0 and 2 until15:18:00. In this example, a value of 2 indicates a high likelihood ofelectrical discharges occurring.

Upon identifying electrical discharge indicators in the transientcharacteristic data, the alert generation module 214 b of servercomputing device 214 generates an alert signal (512) relating to thedetected electrical discharge indicators. In some embodiments, the alertgeneration module 214 b automatically identifies one or more remotedevices that are monitoring (or are otherwise associated with) thebuilding or location where the sensor device 204 is connected to thebranch circuit and automatically transmits) an alert signal to theseremote devices. The remote devices can include computer-based devices,such as mobile phones, tablets, desktop PCs, smart appliances, IoTdevices, smart watches, and the like. The remote devices can alsoinclude horns, sirens, lights, and other audiovisual indicator devices.

In some embodiments, the database 216 includes information related toidentification of the remote devices (e.g., IP address, phone number,email address), and the alert generation module 214 b uses theidentification information to prepare an alert signal for each remotedevice. In some embodiments, the alert generation module 214 b uses anystandard communication protocol or technique, such as packet-baseddelivery (e.g., text messaging, XML, email), circuit-based delivery(e.g., paging, voice messaging), and the like. For example, the alertsignal can take the form of a packet-based communication (e.g., amessage) with a header and body that comprises certain data elements.The alert signal can include information relating to the type of arcfault detected by the system 200, the approximate location of theelectrical discharge activity (e.g., using the time-of-arrivaltechniques described above), an identification of the sensor device 204and its position in the building, and other relevant information (e.g.,identification of appliances or other electrical devices connected tothe same branch circuit, etc.).

FIG. 11 is an exemplary diagram of a user interface of a remotecomputing device (e.g., a mobile phone) for displaying a message to auser based upon receipt of an alert signal. As shown in FIG. 11, an appinstalled on the remote computing device can be coupled to the servercomputing device 214 (e.g., via the Internet) and can be configured toautomatically listen for alert signals pushed from the alert generationmodule 214 b. When an alert signal arrives at the remote computingdevice, the app can automatically display a warning indicator 1102(e.g., a red, flashing graphic or icon) that notifies the user of anelectrical fire hazard. The user interface can also include a log ofprior electrical discharge activity detected (e.g., other events in theuser's home).

In this way, the system and method described herein provide significantadvantages over currently-available arc fault detection technology, inthat the present system can detect small transients occurring in abranch circuit, that may be indicative of electrical discharge activityin the circuit, much earlier and with much more accuracy thantraditional arc fault detectors. The system and method described hereinalso leverage a network-based processing architecture to capture signaldata for a particular branch circuit over time, which can both enableimmediate detection of changes in the electrical activity profile forthe branch circuit and provide useful information to upstream entitiessuch as utility companies and appliance manufacturers about the types ofanomalies that may be occurring in a power system.

Also, it should be appreciated that multiple different sensor devicescan be installed in various buildings across a common electrical gridoperated by a utility provider (e.g., many different homes may have asensor device attached to the building's electrical system to monitorfor electrical discharges as described above). Each of these distributedsensor devices can communicate with one or more centralized servercomputing devices that send data to, and receive data from, the sensordevices. In this configuration, the server computing device can collectelectrical discharge detection data from the sensor devices andaggregate the data for analysis. In one example, the server computingdevice can implement machine learning techniques and algorithms that useelectrical discharge and/or transient data from a plurality of sensordevices installed in different homes and buildings, along with feedbackfrom end users, to improve upon its transient detection andcharacterization algorithms.

In some embodiments, analyzing transient characteristics to identify oneor more electrical discharge indications is enhanced by the applicationof machine learning techniques. Techniques such as boosted trees (asdescribed in xgboost.readthedocs.io/en/latest/tutorials/model.html andFriedman, Jerome H., “Gradient Function Approximation: A GradientBoosting Machine,” The Annals of Statistics, Vol. 29, No. 5 (October2001), pp. 1189-1232, incorporated herein by reference) allow for theautomated selection of various characteristics based on truth data setsfrom houses or buildings with known electrical discharge signals ortruth data sets generated in a lab. Machine learning allows fordevelopment of a more detailed computational model and relationshipsbetween transient characteristics and electrical discharge indicationsthan a human could manually derive. In some embodiments, time seriesfeatures such as autocorrelation lags and kurtosis from these transientcharacteristics are further computed over a tumbling window and providethese features to the machine learning model to provide further contextand improve accuracy. As truth data sets for both electrical dischargeindications or false positives expands, the machine learning models canbe improved, and further developed firmware can be deployed to thesensor in a continuous cycle of improvement.

In addition, the server computing device can distribute updated softwaremethods for identifying transients and transient characteristics (i.e.,in the form of firmware or similar upgrades) to the sensor devices forexecution—thereby automatically synchronizing the sensor network withthe latest algorithms for protecting the home. Also, multiple sensordevices placed in multiple homes can correlate data aboutcommonly-detected voltage waveforms and transients to pinpoint thepotential location of electrical grid problems and communicate to gridoperators or utility providers to take action.

Another aspect of the systems and methods described herein can includethe incorporation of a device that generates signals that look likedangerous electrical discharges, but instead are introduced to theelectrical wiring via a separate electronic device (which in someembodiments, is embedded in the sensor device 204). These signals (alsocalled safety signals) can be used to test the operation of the sensordevice 204, so that any malfunctions or other problems with the sensordevice 204 can be identified. FIG. 12 is a diagram of an exemplarywaveform generated from detection of the safety signals. As shown inFIG. 12, these transients are similar to the types of transients thatwould be created if a dangerous electrical discharge was occurring onthe wiring. It should be noted that these transients are small enough tobe detected by the sensor device 204, but not large enough to trip anAFCI.

Another important aspect of the systems and methods described herein isthe development of an apparatus to test the effectiveness of the sensordevice in detecting fire precursors that occur at any location in ahome. One possible way to accomplish this would be to take a damagedcord that is producing fire precursor pulses to many homes and measurehow well pulse signals are detected from other outlets in the home. Thedrawback of this approach is that it would take a significant amount oftime to send a trained technician that can safely produce the signalsand the study would only measure the signals over a short period oftime—while someone was monitoring the system within the home. Noreasonable individual would welcome a damaged (and hazardous) electricalcord in their house for an extended period of time.

An alternate and more scalable approach is the use of a proxy devicewhich can replay fire signals that are produced and recorded in thelaboratory by real fire precursors. This proxy device allows foreconomical deployment of hundreds of units to varying types of homesacross the country, along with an ability to track signal detectionperformance over long periods of time. FIG. 13 is a block diagram of aproxy device 1300 that can be used in conjunction with the system 200 ofFIG. 2.

As shown in FIG. 13, the proxy device 1300 comprises a flash memorymodule 1302, a CPU 1304, a digital-to-analog signal converter (DAC)1306, and a power amplifier 1308. The device 1300 utilizes the DAC andthe power amplifier (coupled to the electrical system via the powercapacitor) to simulate the fire precursor signals. The DAC 1306 canproduce signals at 120 MHz for short periods of time. The CPU is phaselocked with the zero crossing from the power mains to generate pulsesthat occur at appropriate locations in the phase. Pulse information isread by the CPU from the flash memory and recorded pulses are reproducedthrough the D/A converter and Power Amplifier.

The proxy device 1300, when deployed in a plurality of homes, helps toanswer the following questions:

1) How well do the fire precursor arcing signals travel in a home'selectrical system?

2) How well do the methods and systems described herein identifyelectrical fire signals and distinguish these signals from those createdby other appliances in the home?

To determine how well fire precursor pulse signals travel in the homeelectrical system, the proxy device 1300 and sensor device 204 weredeployed in a test home, along with a test fixture that when pluggedinto an electrical outlet at the home, produces scintillation pulses fordetection. The test fixture included a power cord plugged into astandard wall outlet with Hot, Neutral and Ground conductors. The powercord was spliced to include resistors through which any currentscreating by scintillations or electrical discharges must pass. The endof the power cord was connected to a plug through which various testapparatuses can be connected. The test fixture included a plastic NEMAenclosure through which a damaged extension cord was passed. The damagedelectrical cord can be exposed to various substances which causeelectrical discharges to occur. For example, the substance could begraphite powder, water or a solution of water, soap and salt. Adifferential analog to digital converter measures the voltage across aresistor of known resistance to calculate the current flow through theresistor. Resistors of various sizes were selected to provideappropriate amount of gain depending on the expected peak currentgenerated by exposing the damaged cord to various substances. The testhome is a large single-family home of approximately 4,000 square feet.The home has typical types of electronics equipment including flatscreen televisions, audio equipment and computers. The electronicsequipment is typically protected by surge suppressor power strips.

FIGS. 14A-14E are diagrams showing fire precursor arcing signalscollected from the test home. The graph in FIG. 14A shows 60 seconds ofa measurement of peak analog to digital converter output from twosensors. The sensor device labeled “Same Ting” is on the same 120Vleg/phase, and is on the same branch circuit as the test fixture. Thesensor device labeled “Other Ting” is on the opposite 120V leg/phase ofthe power network on a different branch circuit. Larger amplitudesignals indicate times when the test fixture is creating fire precursorpulses. For example, in FIG. 14A the Same Ting line and Other Ting linejump up to approximately 2400 raw digital units in the area highlightedin the area 1402—indicating that both sensor devices detected a fireprecursor pulse. The graph in FIG. 14B shows the highlighted period 1402from FIG. 14A as zoomed into one second of time.

For the graph in FIG. 14C, data from FIG. 14B is further zoomed in to 20milliseconds of time—which is denoted by the area 1404 in FIG. 14B. InFIG. 14C, the measurement of current 1406 through a 10-ohm resistor onthe hot line of the test fixture is shown. This is the current thatflows when breakdown on the insulator occurs and is observed as anelectrical discharge and is evidenced in the form of heat and light.

FIGS. 14D and 14E show further zoomed-in periods of the current flowthrough the 10-ohm resistor and the resultant waveforms as seen by thesensor devices. The graph in FIG. 14D shows the highlighted period 1408from FIG. 14C as zoomed into 500 microseconds of time, while the graphin FIG. 14E shows the highlighted period 1410 from FIG. 14D as zoomedinto 80 microseconds.

FIG. 15 shows a more detailed version of FIG. 14C, highlighting twotimes where electrical breakdown occurred and which the sensor devicesmeasured current through the 10-ohm resistor. In this example, oneappreciates that electrical breakdown can produce currents that changeslowly over a time period of many microseconds, to currents that startand stop very fast, on the order of nanoseconds to tens of nanoseconds.As can be seen in FIG. 15, the slowly changing currents produce avisible signal 1502 in the sensor device on the same circuit (“SameTing”), but not in the sensor device on a different leg (“Other Ting”),while the fast-changing currents are visible in the signals from bothsensor devices.

FIG. 16 shows a more detailed look at one of the individual pulses froman electrical discharge with some information on the current changerates as the current starts flowing, then experiences severalinterruptions to the current flow until finally being extinguished. Notethat at each fast change in the current, both sensor devices see a sharprise in signal followed by a characteristic ring-down. This demonstratesthat the characteristic of the electrical discharge that makes thebiggest impact on how well signals travel through the electrical networkis the very fast rise time and fall time of the currents. Note that asthe discharge starts, the characteristic ring down signal 1602 appears,but as the current continues at the same level, the sensor device on theopposite phase does not have a strong response. This is an importantfinding for the development of the proxy device, as it means that aslong as the proxy device can simulate the fire precursor fast pulsecurrents rise and fall times, then it can re-create the most relevantpart of the signal and does not need to re-create the longer continuingcurrents.

It should be appreciated that the proxy device 1300, which functions asan arbitrary waveform generator, reproduces laboratory waveformsimperfectly. It can only generate voltage steps at certain digital clockedges and it can only generate steps of certain discrete amplitudes. Thesystem performs lossy compression on the data, preserving only thelargest peaks and preserving their shapes. The impedance of the proxydevice 1300 is not the same as the impedance of a damaged portion ofinsulator, so reflections from the proxy device differ from reflectionsfrom a damaged cable. As a result, it is important to confirm that theproxy device is sufficient to produce signals at the sensor device 204similar to the signals produced by real scintillations in damagedinsulation.

To test this, insulation on a wire was damaged in the laboratory, thecurrents produced by scintillations in the damaged insulation weremeasured, and voltages produced by those scintillations at a sensordevice on a different circuit in the laboratory were simultaneouslymeasured. The proxy device 1300 was then programmed to replay thescintillations that were measured, the damaged insulation was replacedwith the proxy device, and again currents produced at the proxy deviceand voltages produced at a sensor device on a different circuit weresimultaneously measured.

FIG. 17 shows the results of the testing described above. Graph 1702 ashows the current initially measured in the laboratory from damagedinsulation. Graph 1704 a shows the voltage a sensor device measured atanother circuit, produced by the current pulses shown in graph 1702 a.Graph 1706 a shows the current produced by the proxy device, and graph1708a shows the voltage measured by a sensor device on another circuit.Graphs 1702 b, 1704 b, 1706 b, and 1708 b show the same measurements ofgraphs 1702 a, 1704 a, 1706 a and 1708 a, respectively, at a higher timeresolution. The sensor voltages produced by the proxy device are notidentical to those initially measured, but they are similar enough touse.

A second characteristic of fire precursor pulse signals that determineif they travel through the house is the amplitude of the pulse. Largeramplitude pulses produce a larger response in the sensor device 204. Therelationship between the amplitude of the current and the size of thesensor device response can be seen in graphs 1702 b and 1704 b. Largercurrent amplitudes in graph 1702 b are observed as larger sensorresponses in 1704 b. Similarly, larger amplitude currents produced bythe proxy device, result in larger responses at the sensor device (seegraphs 1706 b and 1708 b). Currents on the order of 25 mA are sufficientto produce a response in the sensor device 204 with sufficient signal tonoise ratio to be detected.

Fire precursors can generate thousands of pulses over a single powercycle a large percentage of which are not large enough in amplitude totravel through the electrical infrastructure and be seen by a sensordevice on the opposite leg. The proxy device 1300 is capable ofproducing a limited number of pulses per cycle. The proxy device isprogrammed to focus on generating the largest and fastest pulses whichare expected to be detected throughout the house. In some embodiments,the proxy device 1300 can communicate with a server computing device(e.g., server computing device 214) to notify the server of times whichit is operating and which mode it is operating. In this case, it is easyto correlate the times where the proxy device indicates it is running tosensor device output to verify that the sensor device detects thesignal. In this example, the proxy device 1300 was programmed to createfast pulses with amplitudes that ranged from 0 milliamps to 300milliamps. FIG. 18 shows a histogram of the amplitude of pulses found ina five second set of data that was acquired in the lab, labeled “truth”(dark gray, 1802). Additionally, a histogram of the amplitude of pulsesre-created by the proxy device is shown in light gray (1804). Generally,the counts of proxy pulses is less than from the truth data source. Asdescribed above, this is because of the limits of the proxy devicememory to hold enough data to reproduce every pulse, so the focus is onreproducing the largest pulses.

Note that pulse current amplitudes in the proxy test data set are smallin comparison to the peak amperes which trigger arc fault circuitinterrupters. An AFCI will trip at about 50 amps for a parallel arc and5 amps for a series arc (as described in J. Wafer, “The Evolution of ArcFault Circuit Interruption”, The 51^(st) IEEE HOLM conference onElectrical Contacts, 2005). By detecting pulse currents with these smallamplitudes, the sensor device described herein is able to alert ahomeowner well in advance of the level at which the arcing becomesdangerous and fire hazard is imminent. This timeframe between whenscintillations are detected and the arc grows large enough to bedangerous can be on the order of hours to years (as described inTwibell, J. D., Electricity and Fire, pp. 61-104 in Fire Investigation,N. N. Daéid, ed., CRC Press, Boca Raton, Fla. (2004)).

Having demonstrated that fire precursors create signals on the powernetwork that travel throughout and can be detected by a single sensordevice, the second question for detection efficiency relates to how wellthe sensor device can distinguish between fire precursor signals andman-made or other interfering signals which can be sensed on a powerline. Fire precursor signals exhibit certain characteristics of parallelarcs which are exploited for identification (note that series arcs haveother characteristics that can be exploited for identification—such aslow signal at zero crossings with a large amplitude impulse just outsidethe area of the zero crossing, and glowing connections have yetdifferent characteristics that can be exploited for identification):

1) The fire precursor pulse signals are on average larger near voltagepeaks and weakest, approaching zero near the voltage zero crossings.

2) Pulse signals are randomly distributed in time across multiple cyclesand are randomly distributed across the phase.

FIG. 19 shows a plot of how fire precursor signals look over time. Timeon the plot is increasing from top to bottom. Each row of the plot isrepresentative of a single power cycle with the rising half cycleindicated on the left and the falling half cycle indicated on the right.The scale 1902 indicates the amplitude of HF signals detected at variousplaces in the power cycle phase. For parallel arcs, the HF amplitudeincreases at the peaks of the power cycle and goes to zero at the zerovoltage crossings.

For comparison, FIG. 20 shows the plot of FIG. 19 for a device runningon the electrical network that is generating HF electrical activity. Atypical feature of devices running on an electrical network is that HFelectrical activity is in repeatable places in the phase over longerperiods of time. This is indicated by the vertical lines in the fallinghalf cycle. The sensor algorithms described herein exploit thedifferences between man-made devices which generate predictable andrepeating signals from fire precursor signals which are more variable intime and amplitude.

Building upon the ability to identify individual arcing events asdescribed above, another important aspect of the fire precursordetection and mitigation techniques described herein is the ability togroup the identified individual arcing events into patterns ofelectrical discharge activity and recognize the patterns as potentialfire precursors based upon either comparing the pattern to one or moreknown hazardous electrical discharge patterns (such as a comparison oftransient characteristics of the waveforms) and determining a match orby identifying other conditions (e.g., weather, time of day, continuousoccurrence, power quality events, etc.) that align with the detectedpattern, in order to understand a cause of the electrical dischargeactivity and proactively mitigate the risk of destructive fires. FIG. 21is a diagram of graphs of root mean square (RMS) Voltage changes overtime associated with an appliance (e.g., an oven) turning on and off. Asshown in FIG. 21, the top graph 2102 and the bottom graph 2104 arecaptured from different days (top=Oct. 19, 2021; bottom=Oct. 20, 2021)showing the voltage pattern from the oven turning on and off. As can beappreciated, when the oven turns on, the voltage drops (see, e.g.,reference 2106). The electrical discharge activity associated with theturning on of the oven is shown in the visualization of FIG. 22(described below). When the oven turns on, this creates an electricalactivity pattern 2104 which is the same on Oct. 20, 2021 as theelectrical activity pattern 2102 on Oct. 19, 2021. The RMS Voltage overtime shows the same type of activity which allows system 200 to relatethe turning on of the oven to the arcing pattern.

As described in this specification, an electrical discharge activitypattern can be visualized as a two-dimensional image where each cycle(representing one power cycle) is visualized by a horizontal row ofpixels representing the transient characteristics. A row of pixelsbegins with the upward zero crossing of the power cycle, and thevertical axis represents how the electrical discharge activity patternschange from cycle to cycle. FIG. 22 is an exemplary visualization for anelectrical discharge activity pattern, where the pattern is generatedfrom the voltage changes of FIG. 21 as detected by system 200 due tofaulty wiring of the electric oven. As shown in FIG. 22, image 2202comprises an electrical discharge activity pattern generated from thevoltage changes detected between 13:26:22 to 13:27:22 on Oct. 19, 2021.Image 2204 comprises the electrical discharge activity pattern generatedfrom the voltage changes detected between 03:44:32 and 03:45:32 on Oct.20, 2021. As can be appreciated, the electrical discharge activitypatterns between images 2202 and 2204 are nearly identical. When theoven turns on, each of the activity patterns exhibits an arcingdischarge indication—the bright areas 2206 and 2208, respectively.

FIG. 23 is another exemplary visualization for an electrical dischargeactivity pattern generated by system 200, including an unfiltered viewand a filtered view. As shown in FIG. 23, the left-hand images 2302 aand 2302 b represent an unfiltered view of an electrical dischargeactivity pattern occurring in an electrical system of a home. Theelectrical discharge activity pattern is generated due to any of anumber of deficiencies with the electrical system (such as thoseexamples described previously). Image 2302 a comprises an electricaldischarge activity pattern detected in a one-minute window (e.g.,12:18:40 to 12:19:40). Image 2302 b comprises an electrical dischargeactivity pattern detected in another, later one-minute window (e.g.,12:33:00 to 12:34:00). Similarly, the right-hand images 2304 a and 2304b in FIG. 23 represent the same electrical discharge activity pattern asrepresented in images 2302 a and 2302 b with a filter applied to removerepeating background activity. As can be appreciated, the electricaldischarge activity patterns between corresponding images 2302 a and 2304a, and images 2302 b and 2304 b, are very similar.

FIG. 24A is another exemplary visualization for an electrical dischargeactivity pattern, where the pattern is generated due to arcing at anelectrical panel bus bar in a particular home (Location One) and thepattern occurs at two different time periods. As shown in FIG. 24A,image 2402 comprises an electrical discharge activity pattern detectedbetween 05:20:00 and 05:20:10 on Jul. 28, 2021. Image 2304 comprises theelectrical discharge activity pattern detected between 10:08:10 and10:08:20 on Jul. 30, 2021 at the same home. As can be appreciated, theelectrical discharge activity patterns between images 2402 and 2404 arehighly similar.

FIG. 24B is another exemplary visualization for an electrical dischargeactivity pattern, where the pattern is generated due to arcing at anelectrical panel bus bar in a different home (Location Two) than in FIG.24A. As shown in FIG. 24B, image 2406 comprises an electrical dischargeactivity pattern detected at the home between 18:02:37 and 18:02:50 onSep. 2, 2021. Although brighter (likely due to the sensor devices beingcloser to the electrical panel), the pattern depicted in the image 2406of FIG. 24B is very similar to the pattern depicted in the images 2402,2404 of FIG. 24A. Advantageously, this similarity enables system 200 tostore electrical discharge activity patterns detected at a firstlocation, e.g., in a library of patterns, and compare the patternsstored in the library to electrical discharge activity patterns detectedat a second location—in order to quickly determine that a similar causemay be responsible for the discharge activity at the first location andthe second location. Further details regarding the use of a library ofelectrical discharge activity patterns is provided below.

When one or more electrical discharge activity patterns are detected andrecognized as hazardous, other associated events (e.g., specific devicesturning on or off, grid-related surge or sag power quality events,atmospheric conditions such as solar, temperature, wind, and/orprecipitation, and electrical activity occurring at consistent times ofday and/or for consistent durations during the day) can be identified asaligning with the occurrence of the discharge activity patterns tofurther refine the decision of whether a pattern of electrical dischargeactivity represents a fire risk and to focus on a potential cause of thedischarge pattern. FIG. 25 is an exemplary heat map 2502 of electricaldischarge activity patterns occurring over time, using the electricaldischarge activity pattern of FIG. 22. As shown in FIG. 25, the heat map2502 identifies dates and times of day when the same electricaldischarge activity pattern is occurring (as indicated by the white orlight-colored sections). As can be appreciated, the electrical dischargeactivity pattern occurs at the same time (e.g., around 1200 UTC) eachday. In this example, because the same activity pattern is correlatedwith the same time each day, the system 200 can be configured to raisean alert to the customer and/or the resolution team (as will bedescribed below).

Similarly, FIG. 26 is another exemplary heat map 2602 of electricaldischarge activity patterns occurring over time, using the electricaldischarge activity pattern of FIG. 24A. As shown in FIG. 26, the heatmap 2602 identifies dates and times of day when the electrical dischargeactivity pattern is occurring (as indicated by the white orlight-colored sections). As can be appreciated, the electrical dischargeactivity correlates to a period of continuous activity.

FIG. 27 is a flow diagram of a computerized method 2700 of detectingelectrical discharges that occur in electrical wiring across multiplecycles of a voltage waveform, using the system 200 of FIG. 2. One ormore sensor devices (e.g., sensor device 204) coupled to a branchcircuit (e.g., Branch 1 circuit comprising wires 202) sense (step 2702)a multiple voltage cycle waveform generated by electrical activity onthe branch circuit. For example, because an electrical discharge is animpulse change in current, the signal generated by the electricaldischarge transmits down the electrical wires 202 reflecting around anyjunction. These signals have a delay in returning, which is typicallybased upon the length of the wire. The signals also exhibit a phaseshift, which enables the system 200 to detect transient signals in thewaveform which represent activity that relates to an electricaldischarge (even a very small electrical discharge) on the electricalwires 202 as described herein. In the context of the method of FIG. 27,sensing a multiple voltage cycle waveform advantageously enables thesystem 200 to analyze waveforms from multiple cycles and determinewhether a pattern of electrical activity is occurring, as will bedescribed in greater detail below. The sensor devices 204 transmit themultiple voltage cycle waveform (or at least a portion of the waveformthat contains multiple cycles) to server computing device 214.

Transient analysis module 214 a of server computing device 214 detects(step 2704) one or more patterns of electrical discharge activity in themultiple voltage cycle waveform based upon transient characteristics ofthe waveform across a plurality of cycles. As described above withrespect to FIGS. 5, 6A-6C, 7A-7C, 8A-8C, 9, and 10, the transientanalysis module 214 a can analyze transient characteristics of thereceived waveform to identify electrical discharge activity. In someembodiments, module 214 a can categorize each individual electricaldischarge activity occurrence as a “series” or “parallel” discharge. Ascan be appreciated, series and parallel discharges have distinctivesignatures within a voltage cycle and module 214 a can be configured todetect the distinctive signature of a series and/or parallel dischargein the waveform.

Because in this example the waveform comprises multiple voltage cycles,transient analysis module 214 a further determines whether a same typeor signature of electrical discharge activity occurs in multipleconsecutive or non-consecutive cycles of the waveform—which may indicatea consistent or recurring pattern of electrical discharge activity. Forexample, module 214 a can compare transient characteristics for thewaveform in two different cycles and determine whether the transientcharacteristics match. If a match is determined, module 214 a can groupthe electrical discharge activity from each cycle into a pattern forfurther analysis. Grouping of the electrical discharge activity into oneor more patterns is beneficial to isolating whether arcing is happening,e.g., at the same location in the home over time and/or in similarphysical ways. For example, if water is seeping into electrical wiring,such activity will produce a similar pattern of electrical dischargeactivity occurring over time.

After the transient analysis module 214 a has detected one or morepatterns of electrical discharge activity, module 214 a recognizes (step2706) the detected pattern of electrical discharge activity as a hazardevent. In some embodiments, module 214 a recognizes that the pattern isa hazard event based upon analysis of the transient characteristics ofthe pattern. For example, when the pattern exhibits certain transientcharacteristics (e.g., a ratio of (i) average peak transients in one ormore phase sections of the waveform cycle near a maximum voltage to (ii)average peak transients near a zero crossing of the waveform cycle, andan average of peak transients in one or more of the phase sections neara maximum voltage when the ratio is above a predetermined threshold)that meet or exceed predetermined ranges or threshold values, module 214a can recognize that the pattern is a hazard event and requires furtherinvestigation and analysis.

After recognition that the detected pattern is a hazard event, transientanalysis module 214 a identifies (step 2708) one or more conditionsrelated to the pattern of electrical discharge activity. As can beappreciated, certain types of conditions or events may produceelectrical discharge activity patterns on a home's electrical system, orinfluence already-existing electrical discharge activity patterns, toindicate a potential arcing event or risk of fire. These conditions caninclude atmospheric events in proximity to the location of the home,such as solar conditions, air temperature, wind direction/wind speed,and/or precipitation. For example, if module 214 a determines that thesame pattern of electrical discharge activity indications occurs eachtime it rains at a particular home, it could mean that rainwater isgetting into the home's electrical system and causing the discharges. Inthis case, module 214 a can identify that the occurrence of rain alignswith the electrical discharge activity pattern and determine apossibility that the rain is causing the pattern. As a result, system200 can generate an alert message that prompts the homeowner toinvestigate whether any structural compromise has occurred with the home(e.g., missing roof shingles, cracked siding, leaking foundation, etc.)to impact the safety of the electrical system.

Conditions that generate or affect patterns of electrical dischargeactivity may also include grid-based or local infrastructure-based powerquality events, such as surge events or sag events. Changes in powerquality being received at the home, or occurring within the home, cancause electrical discharge indications to be detected by the system200—in some cases, these discharge indications can have a particularpattern or signature that corresponds to the power quality event. Forpower quality events within the home, this could mean loose wiring in anelectrical panel or on an individual circuit and can point anelectrician to look for a certain type of arcing. This is normallyassociated with an arcing pattern that is series arcing because of afailed conductor. In the case where the arcing is associated withgrid-based power quality events, it could be that the arcing is outsideof the home and could be in wiring between the home and the transformerif only impacting nearby homes on the grid, or if impacting multiplehomes that are far apart to not be on the same transformer, it couldindicate arcing between a substation and the home.

Transient analysis module 214 a can correlate the electrical dischargeactivity indications with power quality event information received from,e.g., a utility provider. For example, when the utility confirms that apower quality event occurred at a specific date and time, and module 214a detects a pattern of electrical discharge activity indications at thesame time, module 214 a can correlate the pattern to the power qualityevent. Therefore, module 214 a can determine whether the electricaldischarge activity indications suggest an arcing event (e.g., certainpower quality events may exacerbate arcing events occurring in faultywiring in a home) or are instead related solely to the power qualityevent based on, e.g., the transient characteristics of the waveform andthe knowledge that a power quality event occurred.

Conditions that generate or affect patterns of electrical dischargeactivity may also include the activation or deactivation of electronicdevices in the home. For example, when a certain device turns on/off, orbegins drawing power (such as an HVAC system, appliance, electricblanket, power supply, etc.), there may be an associated change involtage that is unique to the device. Transient analysis module 214 acan associate patterns of electrical discharge activity with powerchanges caused by devices to determine whether the patterns areindicative of hazardous activity or not. In one example, a homeowner maytrack his or her device usage over time and provide that information tosystem 200 for correlation to electrical discharge activity. In anotherexample, sensor device 204 and/or server computing device 214 can beconfigured to automatically detect changes in voltage on the circuitand/or home electrical system and associate those changes with knownsignatures of electronic devices.

Any of the above conditions that generate or affect patterns ofelectrical discharge activity can include a time of day and/or durationof the event to enable more efficient identification of hazardouselectrical discharge activity captured by system 200. For example,transient analysis module 214 a can monitor the electrical dischargeactivity and determine that the same electrical discharge activitypattern occurs every day at a prescribed time and for a consistentduration. In another example, module 214 a can determine that anelectrical discharge activity pattern continuously occurs. Over time,module 214 a can correlate this pattern with one or more conditions todetermine whether a single cause of the activity can be determined. Incases where the pattern occurs consistently but cannot be reasonablycorrelated to any conditions that suggest the activity does not comprisearcing in the electrical system, module 214 a can determine that anarcing event is likely occurring and generate a notification message.

After identification of conditions related to the electrical dischargeactivity pattern, alert generation module 214 b of server computingdevice 214 generates (step 2710) one or more alert messages based uponthe detected patterns and identified conditions. Module 214 b transmitsthe messages to a remote computing device (e.g., a mobile phone, tablet,smart watch, and the like). The remote computing device can, e.g.,display a message or indicator (such as a warning icon) on a screenassociated with the remote computing device based upon receipt of theone or more alert signals. For example, the alert signal can comprise apacket-based message including a corpus of text that indicates adangerous condition or hazard as detected by the system 200. In someembodiments, the alert generation module 214 b transmits the one or morealert signals to one or more of the sensor devices 204. The sensordevices 204 can activate one or more components (e.g., embeddedcomponents in the sensor device) upon receipt of the one or more alertsignals. For example, upon receiving an alert signal from the alertgeneration module 214 b, the sensor device(s) 204 can activate an LEDelement that lights up and/or flashes on the exterior of the sensordevice 204 to indicate that a dangerous condition or hazard has beendetected by the system 200. In some embodiments, alert generation module214 b generates alert messages upon determining a risk of fireassociated with one or more of the classified patterns and transmits thealert messages to a remote computing device when the risk of fire is ator above a predetermined threshold.

FIG. 28 is a diagram of an exemplary use case timeline 2800 fordetecting electrical discharge patterns that occur in electrical wiringacross multiple cycles of a voltage waveform and identifying relatedconditions, using the system 200 of FIG. 2. The exemplary use casedescribed with respect to FIG. 28 involves the periodic detection of aunique electrical discharge pattern that system 200 has not previouslydetected. As shown in FIG. 28, at Time t1, a rain event occurs at a homewhere the sensor devices are installed. As can be appreciated, due todeficiencies in home construction or maintenance, rainwater may seepinto the interior of the home and come in contact with electrical wiringto cause arcing (also called wet tracking). As the wet tracking occurs,sensor devices 204 installed in the home sense a multiple cycle voltagewaveform generated by electrical activity on the circuit (which includesthe wet tracking activity) and transient analysis module 214 a detects aunique pattern of electrical discharge activity generated from the wettracking. In this case, module 214 a has not previously detected thesame or similar electrical discharge activity pattern. Module 214 aapplies a label to the detected pattern (e.g., “A”) and stores thelabeled pattern in, e.g., database 216 for future reference. In someembodiments, module 214 a also stores metadata associated with thelabeled pattern, including but not limited to customer information,sensor device information (e.g., IP address, MAC address), location ofthe home, date/time when the pattern is detected, other conditions(e.g., atmospheric conditions at the home, power quality conditions onthe grid servicing the home), etc.

Later, at Time t2, another rain event occurs at the location and module214 a detects pattern “A” again. Module 214 a determines that thepattern detected at t2 matches the pattern “A” stored in database 216,and module 214 a records a second occurrence of pattern “A.” Thetimespan between t1 and t2 (Timespan s1) can be days, weeks, months,years, etc. Again, at Time t3, another rain event occurs and module 214a detects pattern “A” for a third time. As above, module 214 adetermines that the pattern detected at t3 matches pattern “A,” andmodule 214 a records a third occurrence of pattern “A” (along with someor all of the metadata described above). As can be appreciated, due tothe randomness and variability of rain events, the timespan between t2and t3 (Timespan s2) is a different length than Timespan s1. After Timet3, additional rain events occur and module 214 a detects furtherinstances of the occurrence of pattern “A” at the home with varyingtimespans in between the pattern detection.

Then, at Time tn, another rain event occurs and module 214 a detectselectrical discharge activity corresponding to pattern “A” again. Atthis point, a sufficient number of occurrences of pattern “A” have beendetected to enable module 214 a to identify related conditions thatalign with the occurrences of pattern “A.” For example, at each timethat pattern “A” is detected, module 214 a can determine that it wasraining at the home (e.g., using weather data captured from an externaldata source and tracked over time). As a result, in some embodiments,module 214 a can classify pattern A as a wet tracking event. Alertgeneration module 214 b generates an alert message for transmission tothe homeowner and/or monitoring service, indicating that a potentiallyhazardous pattern of electrical discharge activity has occurred at thehome a plurality of times and each time it was raining at the home.Using this information, the homeowner/monitoring service can coordinatewith an electrician or home repair technician to, e.g., physicallyinspect the home and the wiring system to determine whether any leaksare occurring that cause the discharge activity. In the event that aleak is found and it is determined that wet tracking is causing thedischarge, module 214 a can be configured to store pattern “A” indatabase 216 as a “wet tracking” pattern for reference in the futureagainst other unknown patterns.

As can be appreciated, over time as more and more hazardous electricaldischarge activity is detected, correlated to identified conditions, andmitigated, a library of known hazardous electrical discharge activitypatterns (and associated causes of such discharge activity) can begenerated for use in more efficiently recognizing hazardous dischargesand more quickly generating alert messages for homeowners. As oneexample, a faulty electric blanket produces a unique electricaldischarge activity pattern and this unique pattern can be recognized bysystem 200 in electrical discharge activity captured from another home.Because system 200 has already classified this pattern as a hazard,system 200 can immediately generate an alert message for transmission tothe homeowner without requiring analysis of other conditions (e.g.,weather, time, power quality, etc.) to determine a potential cause.

Advantageously, as mentioned above database 216 can store a library ofknown hazardous electrical discharge activity patterns (such as waveformsnippets, 2D images, and/or transient characteristics), e.g., that werepreviously detected by system 200 and confirmed as relating topreviously discovered and mitigated fire hazards. Examples of such firehazards can include but are not limited to: circuit breaker and/or busbar arcing within an electrical panel of the home, electrical appliances(such as heating pads, electric blankets, space heaters, etc.), andpower supplies for computers or other electronics. Transient analysismodule 214 a can retrieve the known hazardous electrical dischargeactivity patterns from database 216 and compare the known patterns toone or more patterns detected by sensor devices to determine whether oneor more of the known patterns matches the detected patterns. In oneexample, module 214 a can use a fuzzy threshold when determining amatch—e.g., if a certain number of transients in the detected patternmatch transients in the known pattern(s), then module 214 a confirmsthat the patterns match. When a detected pattern matches one of theknown patterns, module 214 a classifies the identified pattern as ahazard event according to the known pattern type.

As explained above, the electrical discharge activity patterns can bevisualized as two-dimensional (2D) images. These images beneficiallyenable module 214 a to utilize artificial intelligence (AI)-based imageclassification techniques to identify matching electrical dischargeactivity patterns. Exemplary AI-based image classification techniquesand models implemented in module 214 a can comprise convolutional neuralnetworks (CNNs), artificial neural networks, k-nearest neighboralgorithms, decision trees, Random Forest algorithms, and/or supportvector machines. Module 214 a can train an image classification modelusing the known hazardous activity patterns, so that when a new, unknowndischarge activity pattern is provided as input to the model, the modelcan generate a classification of the unknown pattern (e.g., hazard, nohazard) as well as identifying one or more known patterns that areclosest to the unknown pattern. It should also be appreciated that knownelectrical discharge activity patterns can be added to database 216 forindividual homes, buildings, electrical systems, etc., and module 214 acan utilize the above-referenced AI-based image classificationtechniques to classify incoming electrical discharge activity patterndata using the library of known patterns. Furthermore, when a truephysical source of arcing is identified (e.g., during mitigationefforts), the library can be annotated with more specific metadata aboutthe identified hazard. In some embodiments, metadata can include thingslike series/parallel discharge, a device type and/or device model thehazard is associated with, etc. This metadata beneficially enables thesystem 200 to, e.g., subsequently identify in future analyses thespecific make(s) and model(s) of devices that are well-known hazards.

FIG. 29 is a flow diagram of a computerized method 2900 of detectingelectrical discharges that occur in electrical wiring across multiplecycles of a voltage waveform based upon known hazardous electricaldischarges, using the system 200 of FIG. 2. As described previously, oneor more sensor devices (e.g., sensor device 204) coupled to a branchcircuit (e.g., Branch 1 circuit comprising wires 202) sense (step 2902)a multiple voltage cycle waveform generated by electrical activity onthe branch circuit, and the sensor devices 204 transmit the multiplevoltage cycle waveform (or at least a portion of the waveform thatcontains multiple cycles) to server computing device 214. Transientanalysis module 214 a of server computing device 214 detects (step 2904)one or more patterns of electrical discharge activity in the multiplevoltage cycle waveform based upon transient characteristics of thewaveform across a plurality of cycles.

After the electrical discharge activity pattern is detected, module 214a can utilize the library of known hazardous discharge activity patternsin database 216 to determine whether the detected pattern is the same asone or more of the known patterns. Module 214 a classifies (step 2906)the detected pattern of electrical discharge activity as a hazard eventbased upon a similarity of the detected pattern to one or more of theknown patterns. In some embodiments, module 214 a can compare atwo-dimensional image of the detected pattern to one or moretwo-dimensional images of known hazardous patterns (as described above)and determine one or more similarities between the patterns (e.g., pixelsimilarity, shape similarity, edge similarity, feature extraction,etc.). For example, module 214 a can convert the two-dimensional imagesinto multidimensional vectors and compare the vectors to determine asimilarity measure. In some cases, the similarity measure can bequantified as a distance measure—such as Euclidean distance, cosinesimilarity, or Hausdorff distance. Two images can be considered matchingwhere the distance measure is within a predetermined range of values,such as between 0.9 and 1.0 (where 1.0 indicates an exact match and 0.0indicates no match).

In the event that the detected pattern matches one or more knownpatterns, module 214 a can retrieve metadata associated with the knownpattern from database 216 (e.g., conditions associated with the pattern,cause of the pattern, etc.) and generate (step 2908) one or more alertmessages based upon the classified pattern and the retrieved metadatafor transmission to the homeowner devices/monitoring service. In someembodiments, the known pattern can correspond to a specific device type(e.g., model, make), specific conditions (e.g., atmospheric conditions,time conditions, power quality conditions), and/or other causativefactors and modules 214 a can include any or all of this information inthe alert message.

In some embodiments, after matching the detected pattern to one or moreknown patterns, transient analysis module 214 a can identify one or moreconditions related to the detected pattern of electrical dischargeactivity and/or known patterns of electrical discharge activity. As canbe appreciated, the identified conditions can be used to enhance thealert message provided to the homeowner/monitoring service, and assistin more precisely determining a potential cause of the discharge. Forexample, the detected pattern may match a known pattern relating topower supply arcing. Module 214 a can further identify that the detectedpattern most often occurs at 8:00 am. Module 214 a correlates thistimestamp with the detected pattern of electrical discharge activity,and alert generation module 214 b can include the timestamp informationin an alert message. The timestamp information provides the homeownerwith additional context regarding the power supply arcing issue and maylead the homeowner to perform specific investigative steps to confirmthe arcing. For example, the homeowner may start his or her work dayeach morning at 8:00 am by turning on a computer connected to the powersupply. With knowledge of a) the power supply arcing and b) thetimestamp, the homeowner can quickly isolate the behavior of turning onthe computer as generating the arcing issue and determine that thecomputer power supply is likely to blame.

In some embodiments, a detected pattern may not match any of the knownhazardous activity patterns. In this case, transient analysis module 214a can transmit the detected pattern to a remote computing device (notshown)—such as a system analyst device—for visual presentation of thedetected pattern to a user. The user can analyze the detected pattern todetermine whether the pattern indicates or suggests a hazard eventand/or a risk of fire (or at least requires further physicalinvestigation with the homeowner to confirm or rule out). The remotecomputing device can transmit the decision made by the user to transientanalysis module 214 a for classification of the detected pattern. Whenthe user determines that the detected pattern is indicative of a hazardevent or risk of fire, module 214 a can store the detected pattern indatabase 216 and in some cases, use the detected pattern for comparisonand analysis of subsequent electrical activity patterns—therebyproducing a self-learning feature for fire detection. Alternatively,when the user determines that the detected pattern is not indicative ofa hazard event (e.g., risk of fire), transient analysis module 214 a candiscard the detected pattern or store the identified pattern in database216. In another example, if the detected pattern does not match any ofthe known arcing patterns, module 214 a can continue by rejecting ordiscarding the detected pattern as a false positive hazard event.

It should be appreciated that given the nature of electrical fireprecursors—small, hidden, and persistent once they start—it is notenough to indicate to a homeowner via an alert message that a hazardexists. In some embodiments, a monitoring service or operations team canreceive the alert message and automatically contact the homeowner tocoordinate a plan to identify the source of the problem and mitigate therisk. Typically, the fire risk is not imminent. In many cases, however,the analysis performed by system 200 has revealed a definitive causetype, supplying the homeowner and monitoring service with keyinformation and shaping their discussion to isolate the source. Mosthomeowners are able to provide some contextual value to isolatefurther—or even outright identify—the precise cause of the arcingevent(s). Therefore, the benefits and advantages of the technologydescribed herein become apparent—enabling the ability to automaticallydetect fire precursor activity in an electrical system, categorize andclassify the activity according to a potential or predicted risk offire, and proactively coordinate with the homeowner, utility, and/orservice professionals to remediate the problem well before any threat ofloss of life or property has arisen.

The above-described techniques can be implemented in digital and/oranalog electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. The implementation can be as acomputer program product, i.e., a computer program tangibly embodied ina machine-readable storage device, for execution by, or to control theoperation of, a data processing apparatus, e.g., a programmableprocessor, a computer, and/or multiple computers. A computer program canbe written in any form of computer or programming language, includingsource code, compiled code, interpreted code and/or machine code, andthe computer program can be deployed in any form, including as astand-alone program or as a subroutine, element, or other unit suitablefor use in a computing environment. A computer program can be deployedto be executed on one computer or on multiple computers at one or moresites.

Method steps can be performed by one or more special-purpose processorsexecuting a computer program to perform functions of the technology byoperating on input data and/or generating output data. Method steps canalso be performed by, and an apparatus can be implemented as, specialpurpose logic circuitry, e.g., a FPGA (field programmable gate array), aFPAA (field-programmable analog array), a CPLD (complex programmablelogic device), a PSoC (Programmable System-on-Chip), ASIP(application-specific instruction-set processor), or an ASIC(application-specific integrated circuit), or the like. Subroutines canrefer to portions of the stored computer program and/or the processor,and/or the special circuitry that implement one or more functions.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital or analog computer.Generally, a processor receives instructions and data from a read-onlymemory or a random access memory or both. The essential elements of acomputer are a processor for executing instructions and one or morememory devices for storing instructions and/or data. Memory devices,such as a cache, can be used to temporarily store data. Memory devicescan also be used for long-term data storage. Generally, a computer alsoincludes, or is operatively coupled to receive data from or transferdata to, or both, one or more mass storage devices for storing data,e.g., magnetic, magneto-optical disks, or optical disks. A computer canalso be operatively coupled to a communications network in order toreceive instructions and/or data from the network and/or to transferinstructions and/or data to the network. Computer-readable storagemediums suitable for embodying computer program instructions and datainclude all forms of volatile and non-volatile memory, including by wayof example semiconductor memory devices, e.g., DRAM, SRAM, EPROM,EEPROM, and flash memory devices; magnetic disks, e.g., internal harddisks or removable disks; magneto-optical disks; and optical disks,e.g., CD, DVD, HD-DVD, and Blu-ray disks. The processor and the memorycan be supplemented by and/or incorporated in special purpose logiccircuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer in communication with a display device,e.g., a CRT (cathode ray tube), plasma, or LCD (liquid crystal display)monitor, for displaying information to the user and a keyboard and apointing device, e.g., a mouse, a trackball, a touchpad, or a motionsensor, by which the user can provide input to the computer (e.g.,interact with a user interface element). Other kinds of devices can beused to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, and/ortactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributed computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The above describedtechniques can be implemented in a distributed computing system thatincludes any combination of such back-end, middleware, or front-endcomponents.

The components of the computing system can be interconnected bytransmission medium, which can include any form or medium of digital oranalog data communication (e.g., a communication network). Transmissionmedium can include one or more packet-based networks and/or one or morecircuit-based networks in any configuration. Packet-based networks caninclude, for example, the Internet, a carrier internet protocol (IP)network (e.g., local area network (LAN), wide area network (WAN), campusarea network (CAN), metropolitan area network (MAN), home area network(HAN)), a private IP network, an IP private branch exchange (IPBX), awireless network (e.g., radio access network (RAN), Bluetooth, Wi-Fi,WiMAX, general packet radio service (GPRS) network, HiperLAN), and/orother packet-based networks. Circuit-based networks can include, forexample, the public switched telephone network (PSTN), a legacy privatebranch exchange (PBX), a wireless network (e.g., RAN, code-divisionmultiple access (CDMA) network, time division multiple access (TDMA)network, global system for mobile communications (GSM) network), and/orother circuit-based networks.

Information transfer over transmission medium can be based on one ormore communication protocols. Communication protocols can include, forexample, Ethernet protocol, Internet Protocol (IP), Voice over IP(VOIP), a Peer-to-Peer (P2P) protocol, Hypertext Transfer Protocol(HTTP), Session Initiation Protocol (SIP), H.323, Media Gateway ControlProtocol (MGCP), Signaling System #7 (SS7), a Global System for MobileCommunications (GSM) protocol, a Push-to-Talk (PTT) protocol, a PTT overCellular (POC) protocol, and/or other communication protocols.

Devices of the computing system can include, for example, a computer, acomputer with a browser device, a telephone, an IP phone, a mobiledevice (e.g., cellular phone, personal digital assistant (PDA) device,laptop computer, electronic mail device), and/or other communicationdevices. The browser device includes, for example, a computer (e.g.,desktop computer, laptop computer) with a World Wide Web browser (e.g.,Microsoft® Internet Explorer® available from Microsoft Corporation,Mozilla® Firefox available from Mozilla Corporation). Mobile computingdevice include, for example, a Blackberry®. IP phones include, forexample, a Cisco® Unified IP Phone 7985G available from Cisco Systems,Inc., and/or a Cisco® Unified Wireless Phone 7920 available from CiscoSystems, Inc.

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

One skilled in the art will realize the invention may be embodied inother specific forms without departing from the spirit or essentialcharacteristics thereof. The foregoing embodiments are therefore to beconsidered in all respects illustrative rather than limiting of theinvention described herein.

What is claimed is:
 1. A computerized method of detecting hazardouselectrical discharge activity patterns in electrical wiring, the methodcomprising: sensing, by one or more sensor devices coupled to a circuit,a multiple voltage cycle waveform generated by electrical activity onthe circuit; detecting, by a computing device, a pattern of electricaldischarge activity occurring in the multiple voltage cycle waveformbased upon transient characteristics of the waveform in each of aplurality of cycles; recognizing, by the computing device, the patternof electrical discharge activity as a hazard event; identifying, by thecomputing device, one or more conditions related to the pattern ofelectrical discharge activity; and generating, by the computing device,one or more alert messages based upon the pattern of electricaldischarge activity and the identified conditions.
 2. The method of claim1, wherein detecting the pattern of electrical discharge activityoccurring in the multiple voltage cycle waveform comprises: detecting aninstance of electrical discharge activity in each of a plurality ofcycles of the multiple voltage cycle waveform; comparing the detectedelectrical discharge activity instances using characteristics of eachelectrical discharge activity instance to determine a match; andgrouping the matching electrical discharge activity instances into apatterns of electrical discharge activity.
 3. The method of claim 1,wherein the transient characteristics comprise one or more of: a ratioof (i) average peak transients in one or more phase sections of thewaveform cycle near a maximum voltage to (ii) average peak transientsnear a zero crossing of the waveform cycle, and an average of peaktransients in one or more of the phase sections near a maximum voltagewhen the ratio is above a predetermined threshold.
 4. The method ofclaim 1, further comprising determining, by the computing device, acause of the pattern of electrical discharge activity using theidentified conditions.
 5. The method of claim 1, wherein the conditionsrelated to the pattern of electrical discharge activity comprise one ormore of: atmospheric conditions at a location of the sensor devices,temporal conditions, electrical activity conditions associated with anelectrical device coupled to the circuit, or power quality conditionsassociated with a power distribution system to which the sensor devicesare coupled.
 6. The method of claim 5, wherein the atmosphericconditions comprise one or more of precipitation, wind activity, solaractivity, or outdoor temperature.
 7. The method of claim 5, wherein thetemporal conditions comprise a singular time of day or a continuousperiod of time.
 8. The method of claim 5, wherein the power qualityconditions are one or more of: a power surge or a power sag.
 9. Themethod of claim 8, wherein the power quality conditions are identifiedon a power distribution system inside a building where the sensordevices and the circuit are located.
 10. The method of claim 8, whereinthe power quality conditions are identified on a power distributionsystem that provides power to a building where the sensor devices andthe circuit are located.
 11. The method of claim 5, wherein theelectrical activity conditions comprise one or more of: the electricaldevice turning on or the electrical device turning off.
 12. The methodof claim 1, further comprising transmitting, by the computing device,the generated alert messages to one or more remote computing devices.13. The method of claim 12, wherein one or more of the sensor devicesreceives at least one of the alert messages and activates an alertindication device embedded in the sensor device upon receiving the alertmessage.
 14. The method of claim 13, wherein the alert indication devicecomprises a light emitting diode (LED).
 15. A system for detectinghazardous electrical discharge activity patterns in electrical wiring,the system comprising: one or more sensor devices coupled to a circuitthat sense a multiple voltage cycle waveform generated by electricalactivity on the circuit; and a computing device that: detects a patternof electrical discharge activity occurring in the multiple voltage cyclewaveform based upon transient characteristics of the waveform in each ofa plurality of cycles; recognizes the pattern of electrical dischargeactivity as a hazard event; identifies one or more conditions related tothe pattern of electrical discharge activity; and generates one or morealert messages based upon the pattern of electrical discharge activityand the identified conditions.
 16. The system of claim 15, whereindetecting the pattern of electrical discharge activity occurring in themultiple voltage cycle waveform comprises: detecting an instance ofelectrical discharge activity in each of a plurality of cycles of themultiple voltage cycle waveform; comparing the detected electricaldischarge activity instances using characteristics of each electricaldischarge activity instance to determine a match; and grouping thematching electrical discharge activity instances into a patterns ofelectrical discharge activity.
 17. The system of claim 15, wherein thetransient characteristics comprise one or more of: a ratio of (i)average peak transients in one or more phase sections of the waveformcycle near a maximum voltage to (ii) average peak transients near a zerocrossing of the waveform cycle, and an average of peak transients in oneor more of the phase sections near a maximum voltage when the ratio isabove a predetermined threshold.
 18. The system of claim 15, wherein thecomputing device determines a cause of the pattern of electricaldischarge activity using the identified conditions.
 19. The system ofclaim 15, wherein the conditions related to the pattern of electricaldischarge activity comprise one or more of: atmospheric conditions at alocation of the sensor devices, temporal conditions, electrical activityconditions associated with an electrical device coupled to the circuit,or power quality conditions associated with a power distribution systemto which the sensor devices are coupled.
 20. The system of claim 19,wherein the atmospheric conditions comprise one or more ofprecipitation, wind activity, solar activity, or outdoor temperature.21. The system of claim 19, wherein the temporal conditions comprise asingular time of day or a continuous period of time.
 22. The system ofclaim 19, wherein the power quality conditions are one or more of: apower surge or a power sag.
 23. The system of claim 22, wherein thepower quality conditions are identified on a power distribution systeminside a building where the sensor devices and the circuit are located.24. The system of claim 22, wherein the power quality conditions areidentified on a power distribution system that provides power to abuilding where the sensor devices and the circuit are located.
 25. Thesystem of claim 19, wherein the electrical activity conditions compriseone or more of: the electrical device turning on or the electricaldevice turning off.
 26. The system of claim 15, wherein the computingdevice transmits the generated alert messages to one or more remotecomputing devices.
 27. The system of claim 26, wherein one or more ofthe sensor devices receives at least one of the alert messages andactivates an alert indication device embedded in the sensor device uponreceiving the alert message.
 28. The method of claim 27, wherein thealert indication device comprises a light emitting diode (LED).
 29. Acomputerized method of detecting hazardous electrical discharge activitypatterns in electrical wiring, the method comprising: sensing, by one ormore sensor devices coupled to a circuit, a multiple voltage cyclewaveform generated by electrical activity on the circuit; detecting, bya computing device, a pattern of electrical discharge activity occurringin the multiple voltage cycle waveform based upon transientcharacteristics of the waveform in each of a plurality of cycles;classifying, by the computing device, the detected pattern of electricaldischarge activity as a hazard event based upon a similarity of thedetected pattern of electrical discharge activity to one or more knownpatterns of hazardous electrical discharge activity; and generating, bythe computing device, one or more alert messages based upon theclassified pattern of electrical discharge activity.
 30. The method ofclaim 29, wherein the one or more known patterns of hazardous electricaldischarge activity include a pattern of circuit breaker arcing within anelectric panel, a pattern of electric device arcing, and a pattern ofpower supply arcing.
 31. The method of claim 29, wherein the one or moreknown patterns of hazardous electrical discharge activity are stored astwo-dimensional images in a database communicatively coupled to thecomputing device.
 32. The method of claim 31, wherein the computingdevice generates a two-dimensional image of the detected pattern ofelectrical discharge activity occurring in the multiple voltage cyclewaveform.
 33. The method of claim 32, wherein classifying the detectedpattern of electrical discharge activity as a hazard event comprisescomparing one or more characteristics of the two-dimensional image ofthe detected pattern of electrical discharge activity to one or morecharacteristics of the two-dimensional images of the known patterns ofhazardous electrical discharge activity to determine the similarity. 34.The method of claim 32, wherein classifying the detected pattern ofelectrical discharge activity as a hazard event comprises executing amachine learning image classification model using the two-dimensionalimage of the detected pattern of electrical discharge activity as inputto generate the similarity to one or more of the two-dimensional imagesof the known patterns of hazardous electrical discharge activity. 35.The method of claim 34, wherein the machine learning imageclassification model is based upon a convolutional neural network. 36.The method of claim 29, further comprising transmitting, by thecomputing device, the generated alert messages to one or more remotecomputing devices.
 37. The method of claim 36, wherein one or more ofthe sensor devices receives at least one of the alert messages andactivates an alert indication device embedded in the sensor device uponreceiving the alert message.
 38. The method of claim 37, wherein thealert indication device comprises a light emitting diode (LED).
 39. Themethod of claim 29, further comprising identifying, by the computingdevice, one or more conditions related to the detected pattern ofelectrical discharge activity.
 40. The method of claim 39, wherein thecomputing device generates the one or more alert messages based upon theclassified pattern of electrical discharge activity and the identifiedconditions.
 41. A system for detecting hazardous electrical dischargeactivity patterns in electrical wiring, the system comprising: one ormore sensor devices coupled to a circuit that sense a multiple voltagecycle waveform generated by electrical activity on the circuit; and acomputing device that: detects a pattern of electrical dischargeactivity occurring in the multiple voltage cycle waveform based upontransient characteristics of the waveform in each of a plurality ofcycles; classifies the detected pattern of electrical discharge activityas a hazard event based upon a similarity of the detected pattern ofelectrical discharge activity to one or more known patterns of hazardouselectrical discharge activity; and generates one or more alert messagesbased upon the classified pattern of electrical discharge activity. 42.The system of claim 41, wherein the one or more known patterns ofhazardous electrical discharge activity include a pattern of circuitbreaker arcing within an electric panel, a pattern of electric devicearcing, and a pattern of power supply arcing.
 43. The system of claim41, wherein the one or more known patterns of hazardous electricaldischarge activity are stored as two-dimensional images in a databasecommunicatively coupled to the computing device.
 44. The system of claim43, wherein the computing device generates a two-dimensional imagecorresponding to the detected pattern of electrical discharge activityoccurring in the multiple voltage cycle waveform.
 45. The system ofclaim 44, wherein classifying the detected pattern of electricaldischarge activity as a hazard event comprises comparing one or morecharacteristics of the two-dimensional image of the detected pattern ofelectrical discharge activity to one or more characteristics of thetwo-dimensional images of the known patterns of hazardous electricaldischarge activity to determine the similarity.
 46. The system of claim44, wherein classifying the detected pattern of electrical dischargeactivity as a hazard event comprises executing a machine learning imageclassification model using the two-dimensional image of the detectedpattern of electrical discharge activity as input to generate thesimilarity to one or more of the two-dimensional images of the knownpatterns of hazardous electrical discharge activity.
 47. The system ofclaim 46, wherein the machine learning image classification model isbased upon a convolutional neural network.
 48. The system of claim 41,wherein the computing device transmits the generated alert messages toone or more remote computing devices.
 49. The system of claim 48,wherein one or more of the sensor devices receives at least one of thealert messages and activates an alert indication device embedded in thesensor device upon receiving the alert message.
 50. The system of claim49, wherein the alert indication device comprises a light emitting diode(LED).
 51. The system of claim 41, further comprising identifying, bythe computing device, one or more conditions related to the detectedpattern of electrical discharge activity.
 52. The system of claim 51,wherein the computing device generates the one or more alert messagesbased upon the classified pattern of electrical discharge activity andthe identified conditions.